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opencc-by-4.0May 2024View details →

Data and code AEM article: Catching some air: A method to spatially quantify aerial triazole resistance in Aspergillus fumigatus

<h2>Name</h2> <p>Catching_some_air</p> <h2><a href="#description"></a>Description</h2> <p>This script project was written to visualise and analyse the data used in the manuscript: Catching some air: A method to spatially quantify aerial triazole resistance in <em>Aspergillus fumigatus</em>.</p> <p>In the R script we load and clean the data from the international air sampling pilot, analyse it, generate figures of the sampled regions, the CFU totals and resistance fractions. The genotyping and phenotyping data of isolated resistant strains.</p> <p>The following files are required to run this R script:</p> <ul> <li>RF_air_IP_cleaned.csv This fine contains total and resistance counts as well as metadata on samples from international air sampling pilot and includes the following variables:</li> </ul> <p>Sample ID: an arbitrary number given to the packages prior to them being handed out</p> <p> </p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>City/Town: City/Town in which the sample was taken</p> <p>Start date: date on which the trap was deployed and the stickers exposed to the air</p> <p>End date: date on which the trap was taken down and the stickers were re-covered and no longer exposed to the air</p> <p>Total.ITR: A. fumigatus CFU count in the permissive layer of the itraconazole-treated plate</p> <p>Res.ITR: CFU count of colonies that had breached the surface of the itraconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.ITR: The itraconazole (~4 mg/L) resistance fraction = Res.ITR/Total.ITR</p> <p>Total.VOR: A. fumigatus CFU count in the permissive layer of the voriconazole-treated plate</p> <p>Res.VOR: CFU count of colonies that had breached the surface of the voriconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.VOR: The voriconazole (~2 mg/L) resistance fraction = Res.VOR/Total.VOR</p> <p>Total control: CFU count on the untreated growth control plate</p> <p>Date.Batch: The date on which proccessing of the sample was started. To be more specific, the date at which Flamingo medium was poured over the seals of the sample and incubation was started.</p> <p>Note: note on the sample based on either information given the participant or observations in the lab.</p> <p>Exclude: Binary to quickly filter out samples that were considered unsuitable for further analysis either because low or high CFU counts. See manuscript for rationale.</p> <p>Lat: Latitude at the centre of the sampled region, does not relate to sample-specific location.</p> <p>Long: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Weather_data_IP_study_nov_dec_jan22_23.csv : contains raw weather data of the sampled regions during the sampling interval of the pilot downloaded from: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview" target="_blank" rel="nofollow noreferrer noopener">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview</a> (see link for full description of the data and the units). Contains the following variables:</li> </ul> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>Wind Nov : 10 m Wind speed (m/S) for the month november 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Dec : 10 m Wind speed (m/S) for the month december 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Jan : 10 m Wind speed (m/S) for the month januari 2023 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>UV Nov: UV radiation at the surface (J/m^2) for the month november 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Dec: UV radiation at the surface (J/m^2) for the month december 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Jan: UV radiation at the surface (J/m^2) for the month januari 2023. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>Temp Nov: Temperature (K) for the month november 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Dec: Temperature (K) for the month december 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Jan: Temperature (K) for the month januari 2023. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Precipitation Nov: Total precipitation (m) for the month november 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Dec: Total precipitation (m) for the month december 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Jan: Total precipitation (m) for the month januari 2023. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>lat_rep: Latitude at the centre of the sampled region, does not relate to sample specific location.</p> <p>lon_rep: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Genotyping_IP_cleaned.csv : contains the TR-type genotypes of the isolated resistant strains Contains the following variable:</li> </ul> <p>Order: Ordering variable included in the file to readily be able to order the isolates by the order in which they were isolated. Contains the following variables:</p> <p>Strain: Strain code with "I" for strains isolated from itraconazole and V for strains isolated from voriconazole followed by a number indicating the order in which they were isolated from the air sample plate.</p> <p>Air sample: The plate/air sample from which the isolate originates</p> <p>Triazole: The triazole treatment the resistant strain grew on can be ITRA (itraconazole) or VORI (voriconazole)</p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <h2><a href="#project-status"></a>Project status</h2> <p>The manuscript has been published in AEM under the DOI: https://doi.org/10.1128/aem.00271-24</p>

Waikato Multi Label Aerial Imagery Dataset 2012

<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken ~5 years prior to the referenced dataset.  Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>

Waikato Multi Label Aerial Imagery Dataset

<p>Effective land use management is crucial for balancing development against environmental sustainability, preservation of biodiversity, and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks to better inform policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region, spanning 25,000 km\(^2\), specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with three supplementary sets. Each image in the main dataset is annotated with 33 fine-grained, multi-labeled classes and approximate segmentation masks of the classes, and the three supplementary datasets cover spatially coincident satellite imagery and aerial imagery five years prior and five years later from the main dataset. </p>

Waikato Multi Label Aerial Imagery Dataset 2023

<p>This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken ~5 years after the referenced dataset.  Below is the description of the referenced dataset</p> <p>Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.</p>

Aerial release success in UAV trial I in Nakasi, Fiji (2018).

<p>wMel infected Aedes aegypti were realease via air using an automated adult release system integrated into a DJI M600 Prohexacopter UAV (DJI, China). Mosquitoes that came out of the automated adult release mechanism output were captured by the output camera, and the size of the release dose was visually estimated. A scoring system, 0 for blank release, 0.5 for partial release with more than 5 mosquitoes and 1 for good release was recorded for each release point. N/A represents release points that were not flown. N/v represent release points that did not have video footage available.</p>

openafl-3.0Dec 2014View details →

Near real-time ultrahigh-resolution imaging from unmanned aerial vehicles for sustainable land use management and biodiversity conservation in semi-arid savanna under regional and global change (SAVMAP)

<p>To prevent aggravation of existing poverty in semi-arid savannas, a comprehensive concept for the sustainable adaptive management and use of these ecosystems under unprecedented conditions is needed. SAVMAP is an innovative, trans-, and inter-disciplinary initiative whose goal is to develop a valuable monitoring tool for both sustainable land-use management and rare species conservation (black rhinoceros) in semi-arid savanna in Namibia. SAVMAP uses near real-time ultrahigh-resolution photographic imaging (NURI) facilitated by unmanned aerial vehicles (UAVs) designed at EPFL.</p>

opencc-by-4.0Apr 2018View details →

Aerial Imagery From Flights in Robotics Simulator

<p>Dataset contains aerial images captured in a simulated 3D environment. Ortho photo images from USGS Aerial Imagery dataset was used as ground view. Gazebo simulator with PX4 flight controller software and a plane model was used to simulate flights at different altitude and trajectories over different maps.</p> <p>Each dataset is provided in a zip file, named under two character and number abbreviation, which can be interpreted by the first letter for map type (U - urban, F - forest), second letter stands for trajectory type (L - straight line, C - circular trajectory, R - rectangular trajectory) and the number stands for altitude in meters, e.g. FC-300, means forest map, circular trajectory at 300 meters altitude. Alongside image data, a text file containing CSV data is included, which contains aircraft attitude and geographical information of each image.</p> <p>Additionally, maps used for simulation environment are included in this dataset, maps of the same area captured on different years are also included, which can be used to evaluate algorithms matching against maps.</p> <p>ROS Publisher node is available in GitHub: <a href="https://github.com/jureviciusr/AIRDatasetPublisher">https://github.com/jureviciusr/AIRDatasetPublisher</a></p>

Quantifying pine processionary moth defoliation in a pine-oak mixed forest using unmanned aerial systems and multispectral imagery (dataset, paper published in PLOS ONE)

<p>Data processed to analyze pine processionary moth defoliation.</p> <p>Digital surface model and orthomosaics derived from UAS</p>

openother-openAug 2019View details →

Data and code for: Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages

<p><strong>Data and Code for the article:</strong></p> <p><strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong><br>   <br> <em>Jonas O. Wolff, Gustavo B. Paterno, Daniele Liprandi, Martín J. Ramírez, Federico Bosia, Arie van der Meijden, Peter Michalik, Helen M. Smith, Braxton R. Jones, Alexandra M. Ravelo, Nicola Pugno and Marie E. Herberstein</em><br>   <br> Journal: <strong>Evolution</strong>  <br> DOI:  <a href="https://doi.org/10.1111/evo.13834">https://doi.org/10.1111/evo.13834</a>  </p> <p>Github repository: https://github.com/paternogbc/Wolff_et_al_Evolution_aerial_spider_webs</p> <p><br> When using the <strong>data available</strong> in this repository, please cite the original publication.  </p> <p>Contact jonas.wolff@mq.edu.au for any further information.  </p> <p>Wolff, J. O., Paterno, G. B., Liprandi, D. , Ramírez, M. J., Bosia, F. , der Meijden, A. , Michalik, P. , Smith, H. M., Jones, B. R., Ravelo, A. M., Pugno, N. and Herberstein, M. E. (2019), <strong>Evolution of aerial spider webs coincided with repeated structural optimization of silk anchorages</strong>. Evolution. Accepted Author Manuscript. doi:10.1111/evo.13834</p>

opencc-by-4.0Nov 2018View details →

Bridge Inspecting with Unmanned Aerial Vehicles R&D

<p>Corresponding data set for Tran-SET Project No. 17STLSU11. Abstract of the final report is stated below for reference:</p> <p>"The project achieves through research including literature, on site interviews, and experimentation: 1) a recommendation for a UAV-based system to practically assist in routine bridge inspection work in the State of Louisiana, 2) the identification and description of advantages, disadvantages, and limitations in the use of UAVs for routing bridge inspection work in Louisiana, and 3) provided recommendations for future work. The Yuneec H520 aircraft and its E90 camera are recommended, as is the need for a boat to be included as part of the system. The recommended system has advantages in reaching portions of the bridge that are difficult to reach by human inspectors and includes sufficient image resolution to assist the bridge inspection process. A disadvantage though, is that of the overburden of regulations both from the FAA and for getting permission to inspect a bridge using a UAV. These regulations my render negligible, any gains in efficiency perceived in the use of UAVs for bridge inspection. Also, the UAV is described by the project as an assistance tool for the manual bridge inspection process and cannot replace the needed work of bridge inspectors, as it has limitations. For example, the UAV cannot perform inspections beneath the bridge deck since it may lose its GPS navigation reference. Likewise, it cannot see beneath the surface to tell of concrete components have subsurface cracks or timbers might be hollow. These tests are still the domain of manual bridge inspection. The project provided recommendations with respect to changes in how inspections should be done using the UAV, i.e. in the pre-inspection phase, needed field studies using the UAV, needed economics alternative-tradeoffs studies, and recommendations for augmenting the aircraft and its instruments. The Second phase, i.e. the Implementation Phase, will utilize the information and educational fruits of the technical research phase for tutorials, seminars and to facilitate feedback surveys with engineering firms, the LADOTD, engineering societies, and students."</p>

opencc-by-4.0Mar 2024View details →

AIDERv2 (Aerial Image Dataset for Emergency Response Applications)

<div>SUMMARY OF DATASET</div> <div> </div> <div>• This dataset consist of 167723 aerial images divided into 4 classes.</div> <div> </div> <div>• The dataset contains three commonly occurring natural disasters</div> <div>earthquake/collapsed buildings, flood, wildfire/fire, and a normal class; do not reflect any disaster</div> <div> </div> <div>• The images can be loaded as numpy arrays using Python programming language and then used to train a Convolutional Neural Network to detect natural disasters from aerial images.</div> <div> </div> <div>• The images are resized to 224x224x3 (heighty,width,channel number) when loaded as numpy arrays.</div> <div> </div> <div>• The dataset is an extension of the AIDER dataset (Aerial Image Dataset for Emergency Response Applications). </div> <div> </div> <div>• Additional images were collected by open source databases and extracted images as frames of videos downloaded from YouTube. </div> <div> </div> <div> </div> <div>The table below shows the number of images in each set.</div> <div> </div> <div>                              Train     Validation     Test     Total</div> <div>     Earthquakes     1927     239                239     2405</div> <div>              Floods     4063     505                502     5070</div> <div>                   Fire     3509     439                436     4384</div> <div>            Normal     3900     487                477     4864</div> <div>                 Total     13399   1670              1654   16723</div> <div> </div> <div> </div> <div>If you use this dataset please cite the following publications:</div> <div> </div> <div>[1] Shianios, D., Kyrkou, C., Kolios, P.S. (2023). A Benchmark and Investigation of Deep-Learning-Based Techniques for Detecting Natural Disasters in Aerial Images. In: Tsapatsoulis, N., <em>et al.</em> Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14185. Springer, Cham. https://doi.org/10.1007/978-3-031-44240-7_24</div> <div>Link: https://link.springer.com/chapter/10.1007/978-3-031-44240-7_24</div> <div> </div> <div>[2] D. Shianios, P. Kolios, C. Kyrkou, "DiRecNetV2: A Transformer-Enhanced Network for Aerial Disaster Recognition", SN Computer Science, 2024 (Accepted to Appear)</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATASET FOLDERS FORMAT</div> <div> </div> <div>└───data</div> <div>│   │</div> <div>│   └───Dataset_Images</div> <div>│       │   └───Train</div> <div>│   │   │    |    └───Earthquake</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Flood</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Normal</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Wildfire</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│       │   └───Val</div> <div>│   │   │    |    └───Earthquake</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Flood</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Normal</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Wildfire</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│       │   └───Test</div> <div>│   │   │    |    └───Earthquake</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Flood</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Normal</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div>│   │   │    |    └───Wildfire</div> <div>│   │   │    | img (1).jpg</div> <div>│   │   │    | img (2).jpg</div> <div>│   │   │    | .....</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div> <div>DATA SOURCES AND DATA COLLECTION</div> <div> </div> <div>OPEN SOURCE DATABASES</div> <div> </div> <div>└───AIDER </div> <div>https://zenodo.org/record/3888300#.Yuu11nZBxD-</div> <div>Kyrkou, C. and Theocharides, T., 2020. EmergencyNet: Efficient aerial image classification for drone-based emergency monitoring using atrous convolutional feature fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.1687-1699.</div> <div> </div> <div>└───ERA </div> <div>https://lcmou.github.io/ERA_Dataset/</div> <div>Mou, L., Hua, Y., Jin, P. and Zhu, X.X., 2020. Era: A data set and deep learning benchmark for event recognition in aerial videos [software and data sets]. IEEE Geoscience and Remote Sensing Magazine, 8(4), pp.125-133.</div> <div>@article{eradataset,</div> <div>        title = {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},</div> <div>        author = {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},</div> <div>        journal = {IEEE Geoscience and Remote Sensing Magazine},</div> <div>        year = {in press}</div> <div>}</div> <div> </div> <div> </div> <div> </div> <div>└───ISBDA</div> <div>https://drive.google.com/file/d/1kEKJ8kr1aScXz_1El7Mn-Yi0ANducQIW/view</div> <div>Zhu, X., Liang, J. and Hauptmann, A., 2021. Msnet: A multilevel instance segmentation network for natural disaster damage assessment in aerial videos. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 2023-2032).</div> <div>@misc{zhu2020msnet,</div> <div>    title={MSNet: A Multilevel Instance Segmentation Network for Natural Disaster Damage Assessment in Aerial Videos},</div> <div>    author={Xiaoyu Zhu and Junwei Liang and Alexander Hauptmann},</div> <div>    year={2020},</div> <div>    eprint={2006.16479},</div> <div>    archivePrefix={arXiv},</div> <div>    primaryClass={cs.CV}</div> <div>}</div> <div> </div> <div> </div> <div>└───Floods 2013</div> <div>https://github.com/cvjena/eu-flood-dataset</div> <div>Barz, B., Schröter, K., Münch, M., Yang, B., Unger, A., Dransch, D. and Denzler, J., 2019. Enhancing flood impact analysis using interactive retrieval of social media images. arXiv preprint arXiv:1908.03361.</div> <div>@article{barz2019enhancing,</div> <div>  title={Enhancing flood impact analysis using interactive retrieval of social media images},</div> <div>  author={Barz, Bj{\"o}rn and Schr{\"o}ter, Kai and M{\"u}nch, Moritz and Yang, Bin and Unger, Andrea and Dransch, Doris and Denzler, Joachim},</div> <div>  journal={arXiv preprint arXiv:1908.03361},</div> <div>  year={2019}</div> <div>}</div> <div> </div> <div>└───Wildfire Research</div> <div>http://wildfire.fesb.hr/index.php?option=com_content&view=article&id=58&Itemid=54</div> <div> </div> <div> </div> <div>└───PyImages</div> <div>https://drive.google.com/file/d/1NvTyhUsrFbL91E10EPm38IjoCg6E2c6q/view</div> <div>The dataset was curated by PyImageSearch reader, Gautam Kumar.</div> <div> </div> <div> </div> <div> </div> <div> </div> <div>YOUTUBE VIDEOS</div> <div> </div> <div>└───Collapsed Buildings/Earthquakes</div> <div>• https://www.youtube.com/watch?v=TMow3WPcZrQ&t=133s&ab_channel=GORKHALYFOUNDATION</div> <div>• https://www.youtube.com/watch?v=_HT0tYKKjBI&t=47s&ab_channel=Effect.org</div> <div>• https://www.youtube.com/watch?v=rkb3y6K3waU</div> <div>• https://www.youtube.com/watch?v=yir6ArRZY4o&t=109s&ab_channel=UnicefUK</div> <div>• https://www.youtube.com/watch?v=CM9APmIR9Fk&ab_channel=ToonsZilla</div> <div>• https://www.youtube.com/watch?v=tmx2w6drAeU&ab_channel=AssociatedPress</div> <div>• https://www.youtube.com/watch?v=kuSEe8Emwrk&ab_channel=BloombergQuicktake%3ANow</div> <div>• https://www.youtube.com/watch?v=qoFHA3-m5ag&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=MM3PToqEPhQ&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=zB_-TRnGuZE&ab_channel=DISASTERNEWS</div> <div>• https://www.youtube.com/watch?v=TqAMQQOEsBs&ab_channel=WHAS11</div> <div>• https://www.youtube.com/watch?v=0ixjTt-jmok&ab_channel=EveningStandard</div> <div>• https://www.youtube.com/watch?v=bNGA8Ms3d70&ab_channel=CatersClips</div> <div>• https://www.youtube.com/watch?v=wJ-2d5t23Lg&ab_channel=DailyDose</div> <div>• https://www.youtube.com/watch?v=ewUcI7I6Gf4&ab_channel=NBCNews</div> <div>• https://www.youtube.com/watch?v=Wx1cjOdlMZ4&ab_channel=ABCNews%28Australia%29</div> <div>• https://www.youtube.com/watch?v=jiMK_sVmbXk&t=12s&ab_channel=NewChinaTV  </div> <div>• https://www.youtube.com/watch?v=M9au_9A2YRo&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=i6Lh8IXPjso&ab_channel=TheSun</div> <div>• https://www.youtube.com/watch?v=CKwxEr3I4Y8&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=hxqzcajBCNg&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=3&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=2GEeTDuf9mI&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=6&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=bDOuZWxIyNQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=9&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=vzoSADijLCQ&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=15&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=ZaL1fldTEAk&list=RDCMUCD3KREyo3IqCLBC-4khGgIw&index=17&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=QSV81FdilZE&ab_channel=GlobalNews</div> <div>• https://www.youtube.com/watch?v=KgOk9otW1Bg&ab_channel=EricFeijten</div> <div> </div> <div> </div> <div> </div> <div>└───Floods</div> <div>• https://www.youtube.com/watch?v=DJqgv8Sa5bA&t=317s&ab_channel=7NEWSAustralia</div> <div>• https://www.youtube.com/watch?v=w5FintiCLJU&t=9s&ab_channel=GuardianNews</div> <div>• https://www.youtube.com/watch?v=HjMymNN6Ajc&t=143s&ab_channel=BioLogicTreeServices</div> <div>• https://www.youtube.com/watch?v=Tmba18C94C8&ab_channel=AL.com</div> <div>• https://www.youtube.com/watch?v=Dqvpv4Vg4lk&t=63s&ab_channel=ElevenEleven</div> <div>• https://www.youtube.com/watch?v=N7QGicNtN2A&ab_channel=PKSVideoProductions</div> <div>• https://www.youtube.com/watch?v=8CHagyQG16Q&ab_channel=Stolly-Sven</div> <div>• https://www.youtube.com/watch?v=vjH3zFqdzcE&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=GFw89UB4fE8&ab_channel=BenChilders</div> <div>• https://www.youtube.com/watch?v=heP3LEJ_NkE&ab_channel=7NEWSAustralia</div> <div> </div> <div> </div> <div> </div> <div>└───Fires</div> <div>• https://www.youtube.com/watch?v=gbM_NPx2GPc&t=201s&ab_channel=WXChasing</div> <div>• https://www.youtube.com/watch?v=M97sJdyeEM4&t=72s&ab_channel=Sanuck176</div> <div>• https://www.youtube.com/watch?v=1Z2K6lDt76M&t=557s&ab_channel=TheRelaxationChannel</div> <div> </div> <div>└───Normal</div> <div>• https://www.youtube.com/watch?v=SyxjsuNHWhM&t=328s&ab_channel=OneManWolfPack</div> <div>• https://www.youtube.com/watch?v=f1PTWsBtrtc&ab_channel=ChernobylPug</div> <div> </div> <div> </div> <div> </div> <div> </div> <div> </div>

Table 4 in A comparison of image and observer based aerial surveys of narwhal

<p><i>Table 4.</i> Summary statistics of survey results and narwhal abundance estimates from surveys conducted in Melville Bay, West Greenland from 25 to 30 August 2014. Off-effort sightings by the aerial observers were only included in modeling of the detection function. Strata without narwhal sightings were excluded from this table (<i>i.e.</i>, south and northwest). Abundance estimates are corrected for availability bias with <i>â</i> (0) being 0.257 for the aerial observer sightings and 0.22 for the image sightings. Aerial observer sightings were truncated at 1,300 m. CV values are given in parentheses. Ind. = individuals.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th>Uncorrected</th><th></th><th></th><th>Abundance</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td>Encounter</td><td>Mean</td><td>density</td><td>Uncorrected</td><td>Uncorrected</td><td>of individuals</td></tr><tr><th>Platform</th><td>Stratum</td><td>Encounter rate (groups/km)</td><td>rate (ind./km)</td><td>group size</td><td>of groups (groups/km2)</td><td>density of ind. (ind./km2)</td><td>abundance of individuals</td><td>corrected for availability bias</td></tr><tr><th>Aerial</th><td>Northeast</td><td>0.032 (0.86)</td><td>0.084 (0.78)</td><td>2.6 (0.20)</td><td>0.046 (0.93)</td><td>0.115 (0.90)</td><td>300.7 (0.89)</td><td>1,171.0 (0.90)</td></tr><tr><th>observers</th><td>Central</td><td>0.068 (0.50)</td><td>0.261(0.53)</td><td>3.8 (0.11)</td><td>0.046 (0.53)</td><td>0.177 (0.52)</td><td>366.5 (0.52)</td><td>1,426.1 (0.53)</td></tr><tr><th></th><td>All strata</td><td>0.053 (0.41)</td><td>0.187(0.44)</td><td>3.5 (0.10)</td><td>0.046 (0.58)</td><td>0.142 (0.50)</td><td>667.2 (0.50)</td><td>2,596.1 (0.51)</td></tr><tr><th>Images</th><td>Northeast</td><td>0.049 (0.94)</td><td>0.105(0.92)</td><td>2.1 (0.91)</td><td>0.049 (0.94)</td><td>0.105 (0.92)</td><td>217.8 (0.92)</td><td>990 (0.93)</td></tr><tr><th></th><td>Central</td><td>0.059 (0.70)</td><td>0.130(0.61)</td><td>2.2 (0.71)</td><td>0.059 (0.70)</td><td>0.130 (0.61)</td><td>340.0 (0.61)</td><td>1,545.5 (0.61)</td></tr><tr><th></th><td>All strata</td><td>0.055 (0.55)</td><td>0.119(0.50)</td><td>2.2 (0.78)</td><td>0.055 (0.55)</td><td>0.119 (0.50)</td><td>557.8 (0.50)</td><td>2,535.5 (0.51)</td></tr></tbody></table>

Table 2 in A comparison of image and observer based aerial surveys of narwhal

<p><i>Table 2.</i> Summary of sightings by image analysts and/or aerial observers, with and without truncation at 500 m. Where both aerial observer pairs had the same sighting with different group size or perpendicular distance estimates, an averaged value was used.</p><table><tbody><tr><th></th><th></th><th></th><th>Only</th><th></th><th>Sighted by</th><th>Sighted by</th></tr></tbody><tbody><tr><th></th><td>Sighted by</td><td></td><td>sight by</td><td>Only</td><td>image</td><td>image analyst</td></tr><tr><th></th><td>image</td><td>Sighted by</td><td>image</td><td>sighted by</td><td>analyst</td><td>and/or</td></tr><tr><th></th><td>analyst</td><td>observer</td><td>analyst</td><td>observer</td><td>and observer</td><td>observer</td></tr><tr><th>No truncation</th></tr><tr><th>No. of groups</th><td>62</td><td>63</td><td>34</td><td>35</td><td>28</td><td>97</td></tr><tr><th>No. of animals</th><td>135</td><td>227</td><td>62</td><td>124</td><td>88</td><td>274</td></tr><tr><th>Average group size</th><td>2.2</td><td>3.6</td><td>1.8</td><td>3.5</td><td>3.1</td><td>2.8</td></tr><tr><th>Average distance</th><td>227</td><td>580</td><td>237</td><td>839</td><td>329</td><td>454</td></tr><tr><th>Truncation = 500 m</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>No. of groups</th><td>62</td><td>36</td><td>34</td><td>8</td><td>28</td><td>70</td></tr><tr><th>No. of animals</th><td>135</td><td>126</td><td>62</td><td>24</td><td>88</td><td>173</td></tr><tr><th>Average group size</th><td>2.2</td><td>3.5</td><td>1.8</td><td>3.0</td><td>3.1</td><td>2.5</td></tr><tr><th>Average distance</th><td>227</td><td>269</td><td>237</td><td>329</td><td>234</td><td>247</td></tr></tbody></table>

Table 3 in A comparison of image and observer based aerial surveys of narwhal

<p><i>Table 3.</i> AIC values after fitting explanatory variables to the DS and MR models. The final model chosen, Model 1, is given in bold and ΔAIC indicates the difference between the chosen model and the specified model. HN indicates a half-normal form and HR indicates a hazard-rate form for the DS model. The explanatory variables are perpendicular distance (D), group size (S), group size as a factor with three classes (1, 2–5, and <i>≥</i> 6 narwhals) (S3), Beaufort (BF), side of airplane (SP), observer pair (O), and time to next observation <i>≤</i> 10 s (T). Appendix S3 provides an overview of all the models that were developed.</p><table><tbody><tr><th>Model</th><th>DS model</th><th>MR model</th><th>No. of parameters</th><th>AIC</th><th>ΔAIC</th></tr></tbody><tbody><tr><th><b>1</b></th><td><b>HN: D + BF</b></td><td><b>D + O + S</b> <b>3</b></td><td><b>5</b></td><td><b>1,330.91</b></td><td><b>0</b></td></tr><tr><th>2</th><td>HN: D + BF</td><td>D + O + S</td><td>5</td><td>1,332.81</td><td>1.90</td></tr><tr><th>3</th><td>HN: D + BF + S3</td><td>D + O + T</td><td>6</td><td>1,333.10</td><td>2.19</td></tr><tr><th>4</th><td>HN: D + BF</td><td>D + O</td><td>4</td><td>1,333.50</td><td>2.59</td></tr><tr><th>5</th><td>HN: D + BF + S3</td><td>D + O</td><td>5</td><td>1,335.08</td><td>4.17</td></tr><tr><th>6</th><td>HR: D + BF</td><td>D + O + S3</td><td>5</td><td>1,336.97</td><td>6.06</td></tr></tbody></table>

Table 1 in A comparison of image and observer based aerial surveys of narwhal

<p><i>Table 1.</i> Summary of survey effort in the four strata during aerial surveys conducted in Melville Bay from 25 to 30 August 2014.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th></th><th>Aerial observers</th></tr></tbody><tbody><tr><th></th><td></td><td></td><td></td><td></td><td></td><td>Image</td><td></td><td>Aerial observers</td><td>(truncated 500 m)</td></tr><tr><th></th><td></td><td>Planned/</td><td>Length of</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th></th><td>Area</td><td>completed</td><td>surveyed</td><td>Analyzed</td><td>No. of</td><td>No.</td><td>Ave. group</td><td>No. of</td><td>No.</td><td>Ave. group</td><td>No. of</td><td>No.</td><td>Ave. group</td></tr><tr><th>Stratum</th><td>(km2)</td><td>transects (no.)</td><td>transects (km)</td><td>images a</td><td>sightings</td><td>of ind.b</td><td>size <i>n</i> (CV)</td><td>sightings</td><td>of ind.</td><td>size <i>n</i> (CV)</td><td>sightings</td><td>of ind.</td><td>size <i>n</i> (CV)</td></tr><tr><th>NW</th><td>6,376</td><td>5 / 4</td><td>226</td><td>2,066</td><td>0</td><td>0</td><td>0 (0)</td><td>0</td><td>0</td><td>0 (0)</td><td>0</td><td>0</td><td>0 (0)</td></tr><tr><th>NE</th><td>2,076</td><td>11 / 12</td><td>468</td><td>6,572</td><td>23</td><td>49</td><td>2.1 (0.95)</td><td>15</td><td>39</td><td>2.6 (0.77)</td><td>13</td><td>32.5</td><td>2.5 (0.76)</td></tr><tr><th>C</th><td>2,621</td><td>9 / 18</td><td>663</td><td>8,193</td><td>39</td><td>86</td><td>2.2 (0.73)</td><td>48</td><td>188</td><td>3.9 (0.69)</td><td>23</td><td>93.5</td><td>4.1 (0.61)</td></tr><tr><th>S</th><td>3,748</td><td>13 / 10</td><td>575</td><td>4,105</td><td>0</td><td>0</td><td>0 (0)</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><th>All strata</th><td>14,821</td><td>38 / 44</td><td>1,932</td><td>20,936</td><td>62</td><td>135</td><td>2.2 (0.77)</td><td>63</td><td>227</td><td>3.6 (0.72)</td><td>36</td><td>126</td><td>3.5 (0.69)</td></tr></tbody></table><p><sup>a</sup> Including 180 off-effort images that were not used in the analysis. <sup>b</sup> Ind. = individuals.</p>

opencc-by-4.0Jul 2021View details →

Aerial images of sea ice during the Oden_AO2018 campaign (Arctic Ocean 2018: MOCCHA - ACAS - ICE) Central Arctic / North Pole drift station in 2018 on IB ODEN

<p>35 oblique aerial images of sea ice (.jpg) were obtained during helicopter and drone flights during the Oden_AO2018 campaign (Arctic Ocean 2018: MOCCHA - ACAS - ICE) Central Arctic / North Pole drift station in 2018 on IB ODEN. The images display the drifting, melt-pond covered multi-year ice (MYI) floe close to the geographic North Pole in autumn 2018 on which the IB ODEN was anchored to. The images were taken on 14, 16, 28 August and 13 September. The images document the evolution of melt ponds between their fully developed stage in late summer towards their autumn characteristics with a refrozen surface and finally a snow cover.</p> <p>For details, please see the respective publication Anhaus, P., Katlein, C., Nicolaus, M., Hoppmann, M., and Haas, C.: From Bright Windows to Dark Spots: The Evolution of Melt Pond Optical Properties during Refreezing. Currently under review in Geophysical Research Letters. Preprint available <a href="https://doi.org/10.1002/essoar.10507628.1">doi:10.1002/essoar.10507628.1</a></p> <p>For the cruise report, please see Leck, C., Matrai, P. A., Perttu, A.-M., and Gårdfeldt, K. (2019): Expedition report: SWEDARTIC Arctic Ocean 2018. Luleå: Swedish Polar Research Secretariat. <a href="https://nbn-resolving.org/urn:nbn:se:polar:diva-8405">urn:nbn:se:polar:diva-8405</a></p>

openJul 2021View details →

Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets

<p>The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers </p> <p>- Ground Truth</p> <p>- RGB</p> <p>- Thermal</p> <p>The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment</p> <p>Please cite as:</p> <p>Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.</p>

opencc-by-4.0Dec 2022View details →

Reconstructed Aneto glacier surfaces from historic aerial image photogrammetry (1981) and remote sensing techniques (2020, 2021, 2022)

<p>The Aneto Glacier, is the largest glacier in the Pyrenees. Its shrinkage and wastage have been continuous in recent decades, and there are signs of accelerated melting in recent years. In this study, changes in the surface and ice thickness of the Aneto Glacier from 1981 to 2022 are investigated using historical aerial imagery, airborne LiDAR point clouds, and UAV imagery. A GPR survey conducted in 2020, combined with data from photogrammetric analyses, allowed us to reconstruct the current ice thickness and also the existing ice distribution in 1981 and 2011. Over the last 41 years, the total glaciated area has shrunk by 64.7% and the ice thickness has decreased, on average, by 30.5 m. The mean remaining ice thickness in autumn 2022 was 11.9 m, as against the mean thicknesses of 32.9 m, 19.2 m reconstructed for 1981 and 2011 and 15.0 m observed in 2020 respectively. The results demonstrate the critical situation of the glacier, with an imminent segmentation into two smaller ice bodies and no evidence of an accumulation zone. We also found that the occurrence of an extremely hot and dry year, as observed in the 2021–2022 season, leads to a drastic degradation of the glacier, posing a high risk to the persistence of the Aneto Glacier, a situation that could extend to the rest of the Pyrenean glaciers in a relatively short time. </p>

opencc-by-4.0Oct 2022View details →

High-resolution digital elevation models and orthomosaics generated from historical aerial photographs (since the 1960s) of the Bale Mountains in Ethiopia

<p>This dataset contains the results of photogrammetric processing (Digital Elevation Models, Orthomosaics and  subset data used for volumetric calculation and visualization) named: “DEM_1967.7z”: inside the zipped folder “1967_DEM.tif” (digital elevation model produced for the year 1967), “DEM_1984.7z”: inside the zipped folder “1984_DEM.tif” exist (digital elevation model produced for the year 1984). In addition, under “1967_Orthomosaic.7z" and "1984_Orthomosaic.7z” zipped folders, there are orthomosaic files produced namely, “1967_orthomosaic.tif” and "1984_orthomosaic.tif” for the year 1967 and 1984, respectively. The DEMs and Orthomosaics  subset from the results for sites (data example 1 and data example 2) reside under "Data_Examples.zip". Accuracy of the resulted data were assessed and the extracted elevation values are under "Accuracy_assessment.zip".  All DEMs and Orthomosaics are in GeoTIFF format in the Adindan UTM Zone 37 N (EPSG: 20137) projected coordinate system.</p> <p>    Potential application of the presented dataset include:</p> <p>1. watershed management</p> <p>2. analyses of historical landscape change</p> <p>3. detailed mapping and analyses of geological and archaeological features, as well as natural resources</p> <p>4. analyses of geomorphological processes</p> <p>5. socioecological patterns and dynamics</p> <p>6. modelling and planning for telecommunications </p> <p>7. biodiversity research. </p> <p>The inputs for the above resulted DEMs and Orthomosaics are found under Zenodo repository "10.5281/zenodo.7271617". </p>