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3,709 results for “urbanization”
Global urban and rural settlement dataset from 2000 to 2020
<p>Data Update (v2.1): Added WGS84 coordinate-referenced datasets with longitude/latitude gridded partitions for localized access.</p>
Short-Term Synchronous and Asynchronous Ambient Noise Tomography in Urban Areas: Application to Karst Investigation
<p>We used DSurfTomo (<a href="https://github.com/HongjianFang/DSurfTomo">HongjianFang/DSurfTomo: Direct inversion of surface dispersion data based on ray tracing (github.com)</a>) for the tomography.</p> <p>ABC2_2023.dat is the travel time of C1 and C2 cross-correlation functions, used in our tomography.</p> <p>ManualDSurfTomoV1.3.pdf is the manual of DSurfTomo, including the data format description for ABC2_2023.dat.</p> <p>yunqiVs3D.txt is the interpolated 3D Vs model, including longitude, latitude, depth (meter), Vs (m/s).</p> <p>Previous version error: I forgot to write the Vs value.</p>
Dataset for the project "Evaluation of the effects of trace elements from street dust under urban – industrial conditions on the ecophysiology of Acer platanoides L. and Tilia cordata Mill.
<p>Description of the project: The rapid growth of cities, industry and transport has significantly deteriorated environmental quality, especially in areas with the highest population densities. It applies to water, soil, and air, especially in urban areas. Air pollutants include particulate matter (PM), which harms human health. According to WHO reports (2021), PM pollution is the cause of cardiovascular and respiratory diseases, leading to 4.2 million premature deaths worldwide in 2016. Although improving every year, the situation in Poland is still worse than in many European countries. The particulate matter also includes heavy metals, which have a toxic effect on plants. Plants in urban areas are particularly vulnerable, especially trees, which perform several vital functions, including mitigating climate change, filtering pollutants, and improving air quality. The aim of the project was to determine and compare the morphological and physiological responses of selected tree species to particulate pollution stress under urban conditions. Tree leaves are an essential barrier to atmospheric dust by trapping it on their surface. However, this may come at the cost of reduced light absorption, increased leaf temperature, damage to leaf blades and consequently impaired photosynthesis and plant productivity. However, the ability to absorb dust varies between tree species. It depends on the leaf surface structure, and the response may be due to the species' sensitivity to pollutants. Investigations were conducted in the Upper Silesian Industrial Area around various emission sources, such as heavy metal smelters, combined heat and power plants, and busy streets. The research focused on two tree species common in urban areas, the Norway maple (<i>Acer platanoides</i>) and the small-leaved lime (<i>Tilia cordata</i>). It included measurement of heavy metal concentrations in leaf blades and dust collected on their surface, analysis of concentrations of selected pigments and ascorbic acid as markers of environmental stress. The study provided a preliminary assessment of the impact of particulate pollution on tree function under harsh urban conditions and determined the potential of the studied species to reduce atmospheric dust.</p>
Data: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)
<p>This data package has been used to examine the responses of UK butterfly species </p> <p>to different features of the urban environment. 'JC_WCBSmodel.Rdata' presents the</p> <p>butterfly abundance data, and supporting information about </p> <p>species and sites. This data can be fed through the script '04_model_builder.R', to </p> <p>produce the models reported in the research article. '00_functions.R' is a script </p> <p>containing functions which support the modelling process, which is loaded as part of </p> <p>the 04_model_builder script. </p> <p> </p> <p>Summaries of the resulting models are an output of that script - </p> <p>'Butterfly_GAM_Outputs.xlsx'. These are represented graphically in the manuscript, </p> <p>using scripts '06_01_Map'.R:'06_03_Cross_Validation'. '06_04_Model_Metric.R' </p> <p>is a further summary of the .xlsx file, found in the Supplementary Materials. </p> <p>'06_05_graphic_4_twitter.R' produces a condensed version of the figure resulting </p> <p>from the script '06_02_Metric_Summary.R'</p> <p> </p> <p>Dataset descriptions are found in the attached readme.txt</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)
<p>Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.</p>
Supplementary Material 4 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>This video map shows the relative changes in environmental impacts at different locations per year for 2017-2050.</p>
Supplementary Material 1-3 (Spatio-temporal metabolic rifts in urban construction material circularity)
<p>Dataset 1 contains life cycle impact factors used for each stage of the urban metabolism for all spatial levels.</p> <p>Dataset 2 contains the transport distances and modes from supplier locations for all spatial levels.</p> <p>Dataset 3 contains material flow and life cycle impact assessment results for each year (2017-2050) and all spatial levels.</p>
Intra-urban variations in land surface phenology in a semi-arid environment
<p>Data repository for 'Intra-urban variations in land surface phenology in a semi-arid environment', ERL</p> <p>Contact: Ben Crawford, University of Colorado Denver (benjamin.crawford@ucdenver.edu)</p> <p>Data description:</p> <ul> <li>NDVI.zip: <ul> <li>MODIS NDVI geotif rasters for Denver study area</li> <li>Additional metadata provided in subdirectories</li> </ul> </li> <li>LST.zip: <ul> <li>Landsat LST geotif rasters for Denver study area</li> </ul> </li> <li>Tair.zip <ul> <li>Seasonal modeled air temperatures for Denver study area (as described in the manuscript and supplemental information)</li> </ul> </li> <li>Den470_LandCover_250m_WGS.tif <ul> <li>Denver study area 2018 land cover fractions, derived from 1 m data at https://data.drcog.org/</li> </ul> </li> </ul> <p> </p>
Thermal changes along the urban-rural continuums in Southeast Asia
<p>This research has been published in Environmental Research Letters. Please cite it as shown below when using this dataset:</p> <p>Citation:<strong> Zhu, Y., Myint, S., Chen, J., Fan, P., Seto, K., Jain, A. K., Qi, J., & Wang, J. (2025). Thermal changes along the urban-rural continuums in Southeast Asia. Environmental Research Letters. <a href="https://doi.org/10.1088/1748-9326/adcad2">https://doi.org/10.1088/1748-9326/adcad2</a>.</strong></p> <p> </p> <p>Our study focused on 19 cities across 8 countries: Cambodia, Indonesia, Lao PDR, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. The cities included Phnom Penh, Siem Reap, Jakarta, Surabaya, Denpasar, Vientiane, Pakse, KL, Putrajaya, Yangon, NPT, Quezon City, Iloilo, Taytay, Bangkok, Chiang Rai, HCMC, Hanoi, Cantho. </p> <p>To assess the impact of urbanization over the past two decades, we examined the patterns of Land Surface Temperature (LST) changes with Land Use and Land Cover (LULC) changes across 19 cities in SEA along the urban-rural continuums (URCs). These cities include major urban hubs such as Jakarta, Bangkok, and Ho Chi Minh City, as well as medium and smaller cities like Vientiane and Chiang Rai, reflecting a diversity of urban environments and providing a comprehensive sample of SEA’s rapid urban expansion and associated thermal impacts. The boundaries of URCs for each city were defined as an area centered within a 30 km radius from its city center point. This ensured that all cities had the same extent so that the URCs could make the comparison. The city center point was defined as either the center of the central business district or the geometric center of the city’s administrative limits (Estoque et al., 2017). We created 30 ring buffer zones around each city center point at 1 km intervals to analyze LULC and the associated LST change gradient along the URCs. After establishing these buffer zones, we excluded (1) large water bodies or oceans, (2) continuous urban areas beyond the city boundaries—for cities located close to one another, such as Kuala Lumpur and Putrajaya, and Quezon City and Taytay, the city’s URCs were adjusted to exclude overlapping administrative boundaries from neighboring cities, ensuring that the analysis was confined to each city’s specific URCs, and (3) mountains with elevations exceeding 100 m above the mean elevation of each city buffer to minimize the confounding effects of topography on LST variations to ensure that the LST changes we observed were more directly attributable to urban development rather than elevation-related climatic variations. (C. Wang et al., 2016; Z. Wang et al., 2020). These exclusions were implemented to ensure a fair comparison, avoid confusion, and reduce the influence of elevation on the analysis.</p> <p>For URCs, we retrieved LST and NDVI data for the summer months (June, July, and August) between 2000 and 2022 at a 30-meter resolution based on the Google Earth Engine Platform. This period was chosen due to peak heat-related mortality and morbidity (Hsu et al., 2021; Johnson et al., 2009). A total of 5,805 Landsat 5/7/8/9 scenes were collected to ensure full coverage of all 19 cities. Contaminated pixels (e.g., clouds and cloud shadows) were removed via quality assessment bands and Landsat-7 SLC-off stripes were eliminated. NDVI was calculated from the surface reflectance of the Red and Near-Infrared (NIR) bands. LST retrieval from Landsat data was conducted using the Statistical Mono-Window (SMW) algorithm developed by the Climate Monitoring Satellite Application Facility (CM-SAF) (Duguay-Tetzlaff et al., 2015; Freitas et al., 2013; Sun et al., 2004). Further details are provided in the Supplemental materials.</p> <p>The final datasets are LST in 2022, LST Sen's Slope (2000-2022), and NDVI Sen's Slope (2000-2022) and are shared.</p>
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections
<p>This data publication belongs to the following research paper:<br>Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024). <br>https://doi.org/10.1186/s42854-024-00066-2 </p> <p>We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here:</p> <p>Step A - Identification of Records<br>A_01_PRISMA-protocoll.pdf<br>A_02_searchstring.txt<br>A_03_recordsidentified.ris</p> <p>Step B - Screening of Records<br>B_01_recordsscreened-title-keywords.ris<br>B_02_recordsscreened-abstract.ris<br>B_03_recordsscreened-fulltext.ris<br>B_04_studiesincluded.ris<br>B_05_screeningdecisions-overview.xlsx</p> <p>Step C - Qualitative Analysis<br>C_01_codingscheme.xlsx</p> <p> </p>
Milan (ITALY) - Urban Agriculture spatial dataset (years 2007 and 2014)
<p>The data in this dataset is a spatial inventory of <strong>urban agriculture</strong> (UA) carried out in the city of Milan (Italy). UA areas where identified with a multi-step and iterative procedure by using different web-mapping tools, especially multitemporal Google Earth images, and ancillary data such as Google Street View and Bing Maps.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>Despite our best efforts to validate the data, some information may be incorrect.</p> <p><strong>Description of the dataset</strong></p> <p><em><strong>Typologies of UA</strong></em></p> <ul> <li><strong>Residential garden: </strong>Private parcel near single houses (e.g. backyard), villas, buildings, industrial and commercial activities, generally managed by property owners. Cultivation is diversified ranging from leafy vegetables to herbs and fruit trees. Production is intended for self-consumption and/or for hobby purposes.</li> <li><strong>Community garden: </strong>A large area subdivided into multipleplots managed individually (i.e. allotment) or collectively by a group of people. Crop production is intended for self-consumption. Land is assigned by the Municipality; several cases of land cultivated without authorization are also common.</li> <li><strong>Urban farm: </strong>Parcel managed by professional farmers with an intensive and an advanced cropping system. The cultivation can be specialized or oriented to high diversity vegetables. The production is intended for market. The mapping procedure focus on arable crops, horticulture, vineyard, olive groves and orchard.</li> <li><strong>Institutional garden: </strong>Parcel managed by institutions or organizations like schools, religious center, prisons and non-profit organizations. The production is generally intended for self-consumption and less frequently for trade. Several gardens in this category are intended for social purposes (e.g. recreation,education, etc.).</li> <li><strong>Illegal garden: </strong>Parcel isolated, cultivated without authorization organized and managed individually or by a few people. Localization occurs on unused or abandoned areas owned by public bodies or private subjects. The production is intended for self-consumption.</li> <li><strong>Nurseries: </strong>A large area subdivided into multiple plots managed for growing ornamental plants and flowers.</li> </ul> <p><em><strong>Land use typologies</strong></em></p> <ul> <li><strong>Horticulture: </strong>annual crops generally seed sown in spring or summer (tomatoes, lettuce, zucchini, cucumbers, peppers).</li> <li><strong>Vineyard: </strong>grape vines grown in order to produce wine or table grape.</li> <li><strong>Olive groves: </strong>olive trees grown in order to produce olive oil or table olives.</li> <li><strong>Orchards: </strong>mixed trees such as orange, stone fruit, pome fruit, olive trees.</li> <li><strong>Mixed crops: </strong>an area grown with a mix of horticulture crops and fruit trees, not divisible.</li> <li><strong>Nurseries: </strong>ornamental plants, trees, flowers.</li> </ul> <p><strong>Credit</strong></p> <p>Pulighe G., Lupia F. (2019) <em>Multitemporal Geospatial Evaluation of Urban Agriculture and (Non)-Sustainable Food Self-Provisioning in Milan, Italy. </em><strong>Sustainability </strong>2019, <em>11</em>(7), 1846</p> <p>https://www.mdpi.com/2071-1050/11/7/1846</p>
Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"
<p>This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a></p> <p>The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters:</p> <ul> <li> <p>cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs.</p> </li> <li> <p>seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number.</p> </li> <li> <p>EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface.</p> </li> <li> <p>ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970).</p> </li> <li> <p>fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages.</p> </li> <li> <p>port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved.</p> </li> <li> <p>freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message.</p> </li> <li> <p>toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size.</p> </li> <li> <p>dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate.</p> </li> <li> <p>ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic.</p> </li> <li> <p>gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW.</p> <ul> <li> <p>rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>].</p> </li> <li> <p>snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level.</p> </li> <li> <p>ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970).</p> </li> <li> <p>tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity.</p> </li> <li> <p>time—UTC of the received message, with microsecond precision in the ISO 8601 format.</p> </li> <li> <p>gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW.</p> </li> <li> <p>lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> <li> <p>lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity.</p> </li> </ul> </li> <li> <p>bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%).</p> </li> <li> <p>data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements.</p> </li> <li> <p>device_Lat—Latitude of the measurement point gathered from the GPS.</p> </li> <li> <p>device_Lon—Longitude of the measurement point gathered from the GPS.</p> </li> </ul> <p>The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.</p>
Hyperspectral imagery Research Products - Toulouse urban area 2015 (French ANR HYEP project)
<p>The HYEP project (ANR 14-CE22-0016-01) main goal was to propose a panel of methods and processes designed for hyperspectral imaging, which specificity makes a weighty auxiliary for the monitoring of the elements of the urban area. The main results of the project can be found at</p> <ul> <li><a href="http://doi.org/10.1080/01431161.2017.1410247">G. Roussel, C. Weber, X. Briottet and X. Ceamanos, "Comparison of two atmospheric correction methods for the classification of spaceborne urban hyperspectral data depending on the spatial resolution", International Journal of Remote Sensing, vol. 39(5), pp. 1593-1614, 2018.</a></li> <li><a href="http://doi.org/10.1109/ECMSM.2017.7945884">F. Z. Benhalouche, M. S. Karoui, Y. Deville, I. Boukerch, A. Ouamri, ``Multi-sharpening hyperspectral remote sensing data by multiplicative joint-criterion linear-quadratic nonnegative matrix factorization'', Proceedings of the 2017 IEEE International Workshop on Electronics, Control, Measurement, Signals and their application to Mechatronics (ECMSM 2017), May 24-26, 2017, Donostia - San Sebastian</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01903469">Gintautas Mozgeris, Vytaut ̇e Juodkien ̇e, Donatas Jonikaviˇcius, Lina Straigyt ̇e, S ́ebastien Gadal, and Walid Ouerghemmi. Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment. Remote Sensing, 10(10), October 2018.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-02281003">Christiane Weber, Thomas Houet, S ́ebastien Gadal, Rahim Aguejdad, Grzegorz Skupinski, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Cl ́ement Mallet, and Arnaud Le Bris. HYEP HYperspectral imagery for Environmental urban Planning : principaux résultats. In 7ème colloque scientifique du groupe SFPT-GH, Toulouse, France, July 2019. ONERA - SFTP.</a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01852844">Christiane Weber, Rahim Aguejdad, Xavier Briottet, Josselin Aval, Sophie Fabre, Jean Demuynck, Emmanuel Zenou, Yannick Deville, Moussa Sofiane Karoui, Fatima Zohra, Sébastien Gadal, Walid Ouerghemmi, Clément Mallet, Arnaud Le Bris, and Nesrine CHEHATA. Hyperspectral Imagery for Environmental Urban Planning. In IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2018, pages 1628–1631, Valencia, Spain, July 2018a. IEEE.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01854904">Christiane Weber, Rahim Aguejdad, X Briottet, J Avala, S. Fabre, J Demuynck, E Zenou, Y. Deville, M. Karoui, F Z Benhalouche, S Gadal, W Ourghemmi, C. Mallet, A. Le Bris, and N. Chehata. HYPERSPECTRAL IMAGERY FOR ENVIRONMENTAL URBAN PLANNING. In IGARSS 2018, Valencia, Spain, 2018b. </a></li> <li><a href="http://doi.org/10.5194/isprs-archives-XLII-1-W1-167-201">W. Ouerghemmi, A. Le Bris, Nesrine CHEHATA, and Clément Mallet. A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, volume XLII-1/W1, pages 167–174, Hanover, Germany, May 2017. Copernicus GmbH (Copernicus Publications).</a></li> <li><a href="https://hal.inria.fr/hal-02384455">Christiane Weber, Sébastien GADAL, Xavier Briottet, and Clément Mallet. Apport de l’imagerie hyperspectrale pour la planification urbaine. In Karine Emsellem, Diego Moreno, Christine Voiron-Canicio, and Didier Josselin, editors, SAGEO 2016 - Spatial Analysis and Geomatics, Actes de la conférence SAGEO’2016 - Spatial Analysis and GEOmatics, pages 454–462, Nice, France, December 2016. </a></li> <li><a href="https://hal-amu.archives-ouvertes.fr/hal-01359643">Gintautas Mozgeris, S ́ebastien Gadal, Donatas Jonikaviˇcius, Lina Straigyte, Walid Ouerghemmi, and Vytaut ̇e Juodkiene. Hyperspectral and color-infrared imaging from ultra-light aircraft: Potential to recognize tree species in urban environments. In University of California Los Angeles, editor, 8th Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pages 542–546, Los Angeles, United States, August 2016.</a></li> <li><a href="https://hal.inria.fr/hal-02384458">Alexandre Hervieu, Arnaud Le Bris, and Cl ́ement Mallet. Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas. In ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, volume III-3, pages 457–464, Prague, Czech Republic, July 2016.</a></li> <li><a href="https://hal.archives-ouvertes.fr/hal-01888126">Christiane Weber, Thomas Houet, Sebastien GADAL, Rahim Aguejdad, Grzegorz Skupinski, Aziz Serradj, Yannick Deville, Jocelyn Chanussot, Mauro Dalla Mura, Xavier Briottet, Clément Mallet, and Arnaud Le Bris. ANR HYEP ANR 14-CE22-0016-01Hyperspectral imagery for Environmental urban Planning HyepProgramme Mobilité et systèmes urbains 2014. Research report, CNRS UMR TETIS, ESPACE, LETG ; ONERA ; GIPSA-lab ; IRAP ; IGN, October 2018c. </a></li> <li><a href="https://doi.org/10.1080/01431161.2019.1579937">Josselin Aval, Sophie Fabre, Emmanuel Zenou, David Sheeren, Mathieu Fauvel & Xavier Briottet (2019) Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data, International Journal of Remote Sensing, 40:14, 5339-5365, DOI: 10.1080/01431161.2019.1579937 </a></li> <li><a href="https://doi.org/10.3390/rs11111269">Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwenaël Morin, Kim Thanh Nguyen et al. Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification Remote Sensing, MDPI, 2019, 11 (11), pp.1269. ⟨10.3390/rs11111269⟩ hal-02191084v1 </a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191363v1">C. Brabant, Emilien Alvarez-Vanhard, Gwenaël Morin, Thanh Ngoc Nguyen, Achour Laribi et al. Evaluation of dimensional reduction methods on urban vegetation classification performance using hyperspectral data IGARSS 2018, Jul 2018, Valencia, Spain halshs-02191363v1</a></li> <li><a href="https://hal.archives-ouvertes.fr/halshs-02191097v1">Charlotte Brabant, Emilien Alvarez-Vanhard, Thomas Houet. Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques Joint Urban Remote Sensing Event (JURSE 2019), May 2019, Vannes, France halshs-02191097v1</a></li> </ul> <p>This Dataset contains five research outputs of this project that were produced on the basis of Hyperspectral data obtained during an acquisition campaign led on Toulouse (France) urban area on July 2015 using Hyspex instrument which provides 408 spectral bands spread over 0.4 – 2.5 μ. Flight altitude lead to 2 m spatial resolution images.</p> <ul> <li><strong>Fields_samples.7z: </strong> ESRI Shape Format. Supervised SVN classification results for 600 urban trees according to a 3 level nomenclature: leaf type (5 classes), family (12 & 19 classes) and species (14 & 27 classes). The number of classes differ for the two latter as they depend on the minimum number of individuals considered (4 and 10 individuals per class respectively). Trees positions have been acquired using differential GPS and are given with centimetric to decimetric precision. A randomly selected subset of these trees has been used to train machine SVM and Random Forest classification algorithms. Those algorithms were applied to hyperspectral images using a number of classes for family (12 & 19 classes) and species (14 & 27 classes) levels defined according to the minimum number of individuals considered during training/validation process (4 and 10 individuals per class, respectively). Global classification precision for several training subsets is given by Brabant et al, 2019 (<a href="https://www.mdpi.com/470202">https://www.mdpi.com/470202</a>) in terms of averaged overall accuracy (AOA) and averaged kappa index of agreement (AKIA).</li> <li><strong>HySPex-2m.7z: </strong>full hyperspectral VNIR-SWIR ENVI standard image obtained from the coregistration of both VNIR and SWIR ones through a signal aggregation process that allowed to obtain a synthetic VNIR 1.6 m spatial resolution image, with pixels exactly corresponding to natif SWIR image ones. First, a spatially resampled 1.6 m VNIR image was built, where output pixel values were calculated as the average of the VNIR 0.8 m pixel values that spatially contribute to it. Then, ground control points (GCP) were selected over both images and SWIR one was tied to the VNIR 1.6 m image using a bilinear resampling method using ENVI tool. This lead to a 1.6 m spatial resolution full VNIR-SWIR image.</li> <li><strong>HYPXIM-4m.7z, HYPXIM-8m.7z, Sentinel2-10m.7z</strong>: hyperspectral ENVI standard simulated images. Spatial and spectral configurations generated correspond to ESA SENTINEL-2 instrument that was lunched on 2015, and HYPXIM sensor which was under study at that time. </li> </ul>
USM Dataset - A Dataset for Polyphonic Sound Event Tagging in Urban Sound Monitoring Scenarios
<p>This dataset includes 24,000 5-seconds-long polyphonic stereo soundscapes composed of sounds taken from the FSD50k dataset:</p> <p>- Eduardo Fonseca, Xavier Favory, Jordi Pons, Frederic Font, Xavier Serra. FSD50K: an Open Dataset of Human-Labeled Sound Events (<a href="https://arxiv.org/abs/2010.00475">https://arxiv.org/abs/2010.00475</a>)</p> <p>FSD50k samples used in the USM dataset were selected to allow for commercial usage.</p> <p>Find more details about the USM dataset at <a href="https://github.com/jakobabesser/USM">https://github.com/jakobabesser/USM</a></p>
Data and code for Bauer et al. (2022) Urban Ecosystems
<p>Data and code for:</p> <p>Bauer M, Krause M, Heizinger V, Kollmann J (2022) <strong>Using crushed waste bricks for urban greening with contrasting grassland mixtures: no negative effects of brick-augmented substrates varying in soil type, moisture and acid pre-treatment.</strong> – <em>Urban Ecosystems</em> 25, 1369-1378. <a href="https://doi.org/10.1007/s11252-022-01230-x">DOI: 10.1007/s11252-022-01230-x</a></p> <p><a href="https://github.com/markus1bauer/2022_waste_bricks_seedmixtures/blob/master/README.md">GitHub README</a></p>
Urban Sound & Sight (Urbansas) - Labeled set
<p><strong>Urban Sound & Sight (Urbansas): </strong></p> <p>Version 1.0, May 2022</p> <p><strong>Created by</strong><br> Magdalena Fuentes (1, 2), Bea Steers (1, 2), Pablo Zinemanas (3), Martín Rocamora (4), Luca Bondi (5), Julia Wilkins (1, 2), Qianyi Shi (2), Yao Hou (2), Samarjit Das (5), Xavier Serra (3), Juan Pablo Bello (1, 2)<br> 1. Music and Audio Research Lab, New York University<br> 2. Center for Urban Science and Progress, New York University<br> 3. Universitat Pompeu Fabra, Barcelona, Spain<br> 4. Universidad de la República, Montevideo, Uruguay<br> 5. Bosch Research, Pittsburgh, PA, USA</p> <p><strong>Publication</strong></p> <p>If using this data in academic work, please cite the following paper, which presented this dataset:<br> M. Fuentes, B. Steers, P. Zinemanas, M. Rocamora, L. Bondi, J. Wilkins, Q. Shi, Y. Hou, S. Das, X. Serra, J. Bello. “Urban Sound & Sight: Dataset and Benchmark for Audio-Visual Urban Scene Understanding”. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022.</p> <p><strong>Description</strong></p> <p>Urbansas is a dataset for the development and evaluation of machine listening systems for audiovisual spatial urban understanding. One of the main challenges to this field of study is a lack of realistic, labeled data to train and evaluate models on their ability to localize using a combination of audio and video.<br> We set four main goals for creating this dataset: <br> 1. To compile a set of real-field audio-visual recordings;<br> 2. The recordings should be stereo to allow exploring sound localization in the wild;<br> 3. The compilation should be varied in terms of scenes and recording conditions to be meaningful for training and evaluation of machine learning models;<br> 4. The labeled collection should be accompanied by a bigger unlabeled collection with similar characteristics to allow exploring self-supervised learning in urban contexts.<br> Audiovisual data<br> We have compiled and manually annotated Urbansas from two publicly available datasets, plus the addition of unreleased material. The public datasets are the TAU Urban Audio-Visual Scenes 2021 Development dataset (street-traffic subset) and the Montevideo Audio-Visual Dataset (MAVD):</p> <p><br> Wang, Shanshan, et al. "A curated dataset of urban scenes for audio-visual scene analysis." ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021.</p> <p>Zinemanas, Pablo, Pablo Cancela, and Martín Rocamora. "MAVD: A dataset for sound event detection in urban environments." Detection and Classification of Acoustic Scenes and Events, DCASE 2019, New York, NY, USA, 25–26 oct, page 263--267 (2019).</p> <p><br> The TAU dataset consists of 10-second segments of audio and video from different scenes across European cities, traffic being one of the scenes. Only the scenes labeled as traffic were included in Urbansas. MAVD is an audio-visual traffic dataset curated in different locations of Montevideo, Uruguay, with annotations of vehicles and vehicle components sounds (e.g. engine, brakes) for sound event detection. Besides the published datasets, we include a total of 9.5 hours of unpublished material recorded in Montevideo, with the same recording devices of MAVD but including new locations and scenes.</p> <p>Recordings for TAU were acquired using a GoPro Hero 5 (30fps, 1280x720) and a Soundman OKM II Klassik/studio A3 electret binaural in-ear microphone with a Zoom F8 audio recorder (48kHz, 24 bits, stereo). Recordings for MAVD were collected using a GoPro Hero 3 (24fps, 1920x1080) and a SONY PCM-D50 recorder (48kHz, 24 bits, stereo). </p> <p>When compiled in Urbansas, it includes 15 hours of stereo audio and video, stored in separate 10 second MPEG4 (1280x720, 24fps) and WAV (48kHz, 24 bit, 2 channel) files. Both released video datasets are already anonymized to obscure people and license plates, the unpublished MAVD data was anonymized similarly using this anonymizer. We also distribute the 2fps video used for producing the annotations.</p> <p>The audio and video files both share the same filename stem, meaning that they can be associated after removing the parent directory and extension.</p> <p>MAVD:<br> video/<location_id>_<mavd_clip_id>_<clip_split_id>.mp4<br> audio/<location_id>_<mavd_clip_id>_<clip_split_id>.wav</p> <p>TAU:<br> video/<location_id>_<tau_clip_id>.mp4<br> audio/<location_id>_<tau_clip_id>.wav</p> <p><br> where location_id in both cases includes the city and an ID number.</p> <p><br> city & places & clips & mins & frames & labeled mins \\<br> Montevideo & 8 & 4085 & 681 & 980400 & 92 \\<br> Stockholm & 3 & 91 & 15 & 21840 & 2 \\<br> Barcelona & 4 & 144 & 24 & 34560 & 24 \\<br> Helsinki & 4 & 144 & 24 & 34560 & 16 \\<br> Lisbon & 4 & 144 & 24 & 34560 & 19 \\<br> Lyon & 4 & 144 & 24 & 34560 & 6 \\<br> Paris & 4 & 144 & 24 & 34560 & 2 \\<br> Prague & 4 & 144 & 24 & 34560 & 2 \\<br> Vienna & 4 & 144 & 24 & 34560 & 6 \\<br> London & 5 & 144 & 24 & 34560 & 4 \\<br> Milan & 6 & 144 & 24 & 34560 & 6 \\<br> \midrule<br> Total & 50 & 5472 & 912 & 1.3M & 180 \\</p> <p><br> <strong>Annotations</strong></p> <p><br> Of the 15 hours of audio and video, 3 hours of data (1.5 hours TAU, 1.5 hours MAVD) are manually annotated by our team both in audio and image, along with 12 hours of unlabeled data (2.5 hours TAU, 9.5 hours of unpublished material) for the benefit of unsupervised models. The distribution of clips across locations was selected to maximize variance across different scenes. The annotations were collected at 2 frames per second (FPS) as it provided a balance between temporal granularity and clip coverage.</p> <p>The annotation data is contained in video_annotations.csv and audio_annotations.csv. </p> <p><strong>Video Annotations</strong></p> <p>Each row in the video annotations represents a single object in a single frame of the video. The annotation schema is as follows:</p> <ul> <li>frame_id: The index of the frame within the clip the annotation is associated with. This index is 0-based and goes up to 19 (assuming 10-second clips with annotations at 2 FPS)</li> <li>track_id: The ID of the detected instance that identifies the same object across different frames. These IDs are guaranteed to be unique within a clip.</li> <li>x, y, w, h: The top-left corner and width and height of the object’s bounding box in the video. The values are given in absolute coordinates with respect to the image size (1280x720). </li> <li>class_id: The index of the class corresponding to: [0, 1, 2, 3, -1] — see label for the index mapping. The -1 value corresponds to the case where there are no events, but still clip-level annotations, like night and city. When operating on bounding boxes, class_id of -1 should be filtered.</li> <li>label: The label text. This is equivalent to LABELS[class_id], where LABELS=[car, bus, motorbike, truck, -1]. The label -1 has the same role as above.</li> <li>visibility: The visibility of the object. This is 1 unless the object becomes obstructed, where it changes to 0.</li> <li>filename: The file ID of the associated file. This is the file’s path minus the parent directory and extension.</li> <li>city: The city where the clip was collected in.</li> <li>location_id: The specific name of the location. This may include an integer ID following the city name for cases where there are multiple collection points.</li> <li>time: The time (in seconds) of the annotation, relative to the start of the file. Equivalent to frame_id / fps .</li> <li>night: Whether the clip takes place during the day or at night. This value is singular per clip.</li> <li>subset: Which data source the data originally belongs to (TAU or MAVD).</li> </ul> <p><strong>Audio Annotations</strong></p> <p>Each row represents a single object instance, along with the time range that it exists within the clip. The annotation schema is as follows:</p> <ul> <li>filename: The file ID odd the associated audio file. See filename above. </li> <li>class_id, label: See above. Audio has an additional class_id of 4 (label=offscreen) which indicates an off-screen vehicle - meaning a vehicle that is heard but not seen. A class_id of -1 indicates a clip-level annotation for a clip that has no object annotations (an empty scene).</li> <li>non_identifiable_vehicle_sound: True if the region contains the sound of vehicles where individual instances cannot be uniquely identified. </li> <li>start, end: The start and end times (in seconds) of the annotation relative to the file. </li> </ul> <p><strong>Conditions of use</strong></p> <p>Dataset created by Magdalena Fuentes, Bea Steers, Pablo Zinemanas, Martín Rocamora, Luca Bondi, Julia Wilkins, Qianyi Shi, Yao Hou, Samarjit Das, Xavier Serra, and Juan Pablo Bello.</p> <p>The Urbansas dataset is offered free of charge under the following terms:</p> <ul> <li>Urbansas annotations are release under the CC BY 4.0 license</li> <li>Urbansas video and audio replicates the original sources licenses: <ul> <li> MAVD subset is released under CC BY 4.0 </li> <li> TAU subset is released under a Non-Commercial license</li> </ul> </li> </ul> <p><strong>Feedback</strong></p> <p>Please help us improve Urbansas by sending your feedback to:</p> <ul> <li>Magdalena Fuentes: mfuentes@nyu.edu</li> <li>Bea Steers: bsteers@nyu.edu </li> </ul> <p>In case of a problem, please include as many details as possible.</p> <p><strong>Acknowledgments</strong></p> <p>This work was partially supported by the National Science Foundation award 1955357 and Bosch RTC.</p>
Urban land expansion in area with decreased urban sprawl at global, national, and city scales during 2000 to 2020
<p>I used calibrated population density thresholds from the year 2000 and 2020 Worldpop population model to measure area and densities for urban and suburban density classes (≥ 250 humans per km<sup>2</sup>) at global and national scales and both broad multi-city agglomerations and fine city cores.</p>
Paradoxical Urbanism_An urban revolution
<p>Coordinador del Seminario: Carlos A. Navarrete Ulloa.<br> Expositor: Briseida Corzo Rivera</p> <p>Comité Ejecutivo PRONACE-Vivienda<br> Fernando Córdova Canela, Centro Universitario de Arte, Arquitectura y Diseño, Universidad de Guadalajara (UdeG). Francisco Javier Porras Sánchez, Instituto de Investigaciones Dr. José María Luis Mora.<br> Gabriel Castañeda Nolasco, Universidad Autónoma de Chiapas (UNACH). Carlos A. Navarrete Ulloa, Centro Universitario de Tonalá, (UdeG).</p> <p>Exposición realizada en el marco del PRONACE Vivienda en el cual se comenta la lectura:<br> Miles. M. (2021) An urban revolution?. In: Miles, M. (2021). Paradoxical Urbanism: Anti-Urban Currents in Modern Urbanism. Springer, Palgrave Pivot. </p>
Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project
<p>Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript:</p> <blockquote> <p>"Harmonized, gap-filled dataset from 20 urban flux tower sites" </p> <p><a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a></p> </blockquote> <p>Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata. </p> <p><strong>Data can be downloaded from the bottom of this page. </strong></p> <table> <tbody> <tr> <td> <p><strong>Sitename</strong></p> </td> <td> <p><strong>City</strong></p> </td> <td> <p><strong>Country</strong></p> </td> <td> <p><strong>Observed period</strong></p> </td> <td> <p><strong>References</strong></p> </td> </tr> <tr> <td> <p>AU-Preston</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Aug 2003 – Nov 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>AU-SurreyHills</p> </td> <td> <p>Melbourne</p> </td> <td> <p>Australia</p> </td> <td> <p>Feb 2004 – Jul 2004</p> </td> <td> <p>(Coutts et al., 2007a, b)</p> </td> </tr> <tr> <td> <p>CA-Sunset</p> </td> <td> <p>Vancouver</p> </td> <td> <p>Canada</p> </td> <td> <p>Jan 2012 – Dec 2016</p> </td> <td> <p>(Christen et al., 2011; Crawford and Christen, 2015)</p> </td> </tr> <tr> <td> <p>FI-Kumpula</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Karsisto et al., 2016)</p> </td> </tr> <tr> <td> <p>FI-Torni</p> </td> <td> <p>Helsinki</p> </td> <td> <p>Finland</p> </td> <td> <p>Dec 2010 – Dec 2013</p> </td> <td> <p>(Järvi et al., 2018; Nordbo et al., 2013)</p> </td> </tr> <tr> <td> <p>FR-Capitole</p> </td> <td> <p>Toulouse</p> </td> <td> <p>France</p> </td> <td> <p>Feb 2004 – Mar 2005</p> </td> <td> <p>(Masson et al., 2008; Goret et al., 2019)</p> </td> </tr> <tr> <td> <p>GR-HECKOR</p> </td> <td> <p>Heraklion</p> </td> <td> <p>Greece</p> </td> <td> <p>Jun 2019 – Jun 2020</p> </td> <td> <p>(Stagakis et al., 2019)</p> </td> </tr> <tr> <td> <p>JP-Yoyogi</p> </td> <td> <p>Tokyo</p> </td> <td> <p>Japan</p> </td> <td> <p>Mar 2016 – Mar 2020</p> </td> <td> <p>(Hirano et al., 2015; Ishidoya et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Jungnang</p> </td> <td> <p>Seoul</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jan 2017 – Apr 2019</p> </td> <td> <p>(Jo et al., n.d.; Hong et al., 2020)</p> </td> </tr> <tr> <td> <p>KR-Ochang</p> </td> <td> <p>Ochang</p> </td> <td> <p>South Korea</p> </td> <td> <p>Jun 2015 – Jul 2017</p> </td> <td> <p>(Hong et al., 2019, 2020)</p> </td> </tr> <tr> <td> <p>MX-Escandon</p> </td> <td> <p>Mexico City</p> </td> <td> <p>Mexico</p> </td> <td> <p>Jun 2011 – Sep 2012</p> </td> <td> <p>(Velasco et al., 2011, 2014)</p> </td> </tr> <tr> <td> <p>NL-Amsterdam</p> </td> <td> <p>Amsterdam</p> </td> <td> <p>Netherlands</p> </td> <td> <p>Jan 2019 – Oct 2020</p> </td> <td> <p>(Steeneveld et al., 2020)</p> </td> </tr> <tr> <td> <p>PL-Lipowa</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013; Pawlak et al., 2011)</p> </td> </tr> <tr> <td> <p>PL-Narutowicza</p> </td> <td> <p>Łódź</p> </td> <td> <p>Poland</p> </td> <td> <p>Jan 2008 – Dec 2012</p> </td> <td> <p>(Fortuniak et al., 2013, 2006)</p> </td> </tr> <tr> <td> <p>SG-TelokKurau</p> </td> <td> <p>Singapore</p> </td> <td> <p>Singapore</p> </td> <td> <p>Feb 2015 – Feb 2016</p> </td> <td> <p>(Roth et al., 2017)</p> </td> </tr> <tr> <td> <p>UK-KingsCollege</p> </td> <td> <p>London</p> </td> <td> <p>UK</p> </td> <td> <p>Apr 2012 – Jan 2014</p> </td> <td> <p>(Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b)</p> </td> </tr> <tr> <td> <p>UK-Swindon</p> </td> <td> <p>Swindon</p> </td> <td> <p>UK</p> </td> <td> <p>May 2011 – Apr 2013</p> </td> <td> <p>(Ward et al., 2013)</p> </td> </tr> <tr> <td> <p>US-Baltimore</p> </td> <td> <p>Baltimore</p> </td> <td> <p>USA</p> </td> <td> <p>Jan 2002 – Jan 2007</p> </td> <td> <p>(Crawford et al., 2011)</p> </td> </tr> <tr> <td> <p>US-Minneapolis</p> </td> <td> <p>Minneapolis</p> </td> <td> <p>USA</p> </td> <td> <p>Jun 2006 – May 2009</p> </td> <td> <p>(Peters et al., 2011; Menzer and McFadden, 2017)</p> </td> </tr> <tr> <td> <p>US-WestPhoenix</p> </td> <td> <p>Phoenix</p> </td> <td> <p>USA</p> </td> <td> <p>Dec 2011 – Jan 2013</p> </td> <td> <p>(Chow, 2017; Chow et al., 2014)</p> </td> </tr> </tbody> </table> <p>For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>.</p> <p>For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>.</p> <p><strong>Data</strong></p> <p>Two data archives are available on this page.</p> <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> <p><strong>Full collection</strong></p> <p>The full archive includes site folders with:</p> <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> <p><strong>"Obs Only"</strong></p> <p>This archive contains duplicate data from the full collection (observations after QC):</p> <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> <p><strong>Site references</strong></p> <p>Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015.</p> <p>Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017.</p> <p>Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014.</p> <p>Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a.</p> <p>Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b.</p> <p>Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015.</p> <p>Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011.</p> <p>Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006.</p> <p>Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013.</p> <p>Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019.</p> <p>Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015.</p> <p>Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020.</p> <p>Hong, J.-W., Hong, J., Chun, J., Lee, Y. 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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.