Find research datasets worth reusing

opencc-zeroDec 2023View details →

Building footprints Oldenburg derived from aerial imagery

This data set contains about 78000 georeferenced polygons representing all building footprints within the administrative boundaries of the city of Oldenburg, Lower Saxony, Germany. These geometries were created by a deep learning-based image segmentation. The model for this was trained at the State Office of Lower Saxony for Geoinformation and Surveying (LGLN). We publish this data under the CC0 license. You can do whatever you want with it. There are no restrictions. If you do something great with the data set, we'd love to hear about it: <a href="mailto:ki-gebaeudeerkennung@geolabs.atlassian.net">ki-gebaeudeerkennung@geolabs.atlassian.net</a> If you use this dataset, you are welcome to reference it - but you don't have to. Would you like builiding footprints for another area? We'd love to hear about it.

Aerial Multi-Vehicle Detection Dataset

Aerial Multi-Vehicle Detection Dataset: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of  such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments.  The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1]  The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training.  <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Car</td> <td>Bus</td> <td>Truck</td> </tr> <tr> <td>Training</td> <td>7239</td> <td>200301</td> <td>1601</td> <td>6247</td> </tr> <tr> <td>Validation</td> <td>904</td> <td>23397 </td> <td>193 </td> <td>727</td> </tr> <tr> <td>Testing</td> <td>905</td> <td>24715</td> <td>208</td> <td>770</td> </tr> </tbody> </table> It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).   [1] Makrigiorgis, R., 2021. Harpy Data Dataset. [online] Kios.ucy.ac.cy. Available at: <https://www.kios.ucy.ac.cy/harpydata/> [Accessed 22 September 2022].   **NOTE** If you use this dataset in your research/publication please cite us using the following : <blockquote> Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442 </blockquote>

Aerial Vessels Detection Dataset

Aerial Vessels Detection Dataset: The dataset construction involved manually collecting all aerial images of vessels using UAV drones and manually annotated into three classes 'Person', 'Ship', and ''Boat'. The aerial images were collected through manual flights above Cyprus Coasts in Limassol, Famagusta and Larnaca areas. The main purpose of this dataset is to be used for marine monitoring. Capturing footage over large areas and localizing any unwanted vessels entering an area of interest, can aid in localizing refugees that illegally enter a country or manage marine traffic for commercial use. The images are collected in 720p and Full HD (1080p) but are usually resized before training. All images were manually annotated and inspected afterward with the vessels that indicate 'Person' for people detection, 'Boat' for small to medium-sized boats, and 'Ship' for large ships or commercial ships. All annotations were converted into VOC and COCO formats and initially labeled in YOLO, for training in numerous frameworks. The data collection took part in different periods. The dataset includes a total of 10252 images of which 1024 are split for validation, 1025 for testing, and the rest 8203 for training.  <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Person</td> <td>Boat</td> <td>Ship</td> </tr> <tr> <td>Training</td> <td>8203</td> <td>219</td> <td>48550</td> <td>920</td> </tr> <tr> <td>Validation</td> <td>1024</td> <td>7</td> <td>5890</td> <td>143</td> </tr> <tr> <td>Testing</td> <td>1025</td> <td>13</td> <td>5247</td> <td>109</td> </tr> </tbody> </table> It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).   **NOTE** If you use this dataset in your research/publication please cite us using the following : <blockquote> Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Vessels Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7076145 </blockquote>

opencc-by-4.0May 2021View details →

Replication files for: Strongmen Cry Too: The Effect of Aerial Bombing on Voting for The Incumbent in Competitive Autocracies

The NATO bombing of Yugoslavia, which lasted from March 24, 1999 until June 10, 1999, was the largest air campaign in Europe since the bombing of Britain and Germany in World War II. The air raids lasted for 78 days and hit 108 out of 160 municipalities, excluding Kosovo and Montenegro. The bombing was spread out and largely aimed at military barracks, industrial facilities, transportation networks, and communication lines. This repo provides a novel dataset with information on over 1,000 targets in the Federal Republic of Yugoslavia, including the date, location, target type, and fatalities. Included is also R code for the replication of my article "Strongmen Cry Too: The Effect of Aerial Bombing on Voting for The Incumbent in Competitive Autocracies" that was accepted for publication at Journal of Peace Research.

opencc-by-4.0Dec 2022View details →

Aerial Landing Pad Dataset

Landing Pad Dataset: The Landing Pad Image Dataset is a collection of images containing a landing pad, along with object annotations for the landing pad found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for autonomous landing of Unmanned Aerial Vehicles (UAVs). This dataset is intended for use in research and development of UAV landing systems, such as those used in industrial applications. The inclusion of the landing pad annotations allows for the detection and localization of the landing pad in real-time, enabling the UAV to safely and accurately land on the designated landing surface. Noted that some of the images are also taken from a simulation. The dataset consists of the following images and detection objects (Landing Pad and Kios Logo): <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>Landing Pad</td> <td>Kios Logo</td> </tr> <tr> <td>Training</td> <td>961</td> <td>818</td> <td>349</td> </tr> <tr> <td>Validation</td> <td>480</td> <td>425</td> <td>153</td> </tr> <tr> <td>Testing</td> <td>480</td> <td>412</td> <td>167</td> </tr> </tbody> </table> It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).   **NOTE** If you use this dataset in your research/publication please cite us using the following  <blockquote> Rafael Makrigiorgis, Panayiotis Kolios, Christos Kyrkou, & Charalambos Soteriou. (2022). Aerial Landing Pad Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.747756 </blockquote>

opencc-by-4.0Mar 2023View details →

Small Object Aerial Person Detection Dataset

Small Object Aerial Person Detection Dataset: The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.   <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>People</td> </tr> <tr> <td>Training</td> <td>2092</td> <td>40687</td> </tr> <tr> <td>Validation</td> <td>523</td> <td>10589</td> </tr> <tr> <td>Testing</td> <td>521</td> <td>10432</td> </tr> </tbody> </table>   It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

Aerial Power Infrastructure Detection Dataset

Aerial Power Infrastructure Detection Dataset: Autonomous inspection of power networks with Unmanned Aerial Vehicles (UAVs) has recently gained significant scientific attention mainly due to rapid advances in UAV technology. In this context, the UAV must autonomously navigate across the network acquiring high resolution data in a safe and fast manner, which poses challenges especially in cases where the location of infrastructure components, i.e. poles, is not known. The Aerial Power Infrastructure Detection Dataset is constructed aiming to create a repository available to the research community, which can be used for training online detection models, which in turn facilitate UAV localization across the network. Specifically, this dataset is used for implementing the “Pole Detection” process of ICARUS toolkit, which is a vision-based UAV monitoring platform for autonomous inspection of Medium Voltage (MV) power distribution network. Specifically, the UAV is supplied with the best-known coordinates of poles and navigates to the designated location searching for the pole. As soon as the pole is detected, using an one-class detection model, the UAV applies a control procedure to correct its position by aligning directly above the pole. For the training, we used samples containing the T-shaped bar of the pole with the insulators and the top of pole [1]. The dataset consists of top-view images of MV poles from various locations across Cyprus. Images were captured across different seasons to account for a variety of background conditions, such as grass or ground, as well as at different heights to account for variations in the UAV’s height during inspection. Additionally, all annotations were converted into VOC and COCO formats for training in numerous frameworks. The dataset consists of the following images and detection objects (t-bars): <table> <tbody> <tr> <td>Subset</td> <td>Images</td> <td>T-Bars</td> </tr> <tr> <td>Training</td> <td>10760</td> <td>10012</td> </tr> <tr> <td>Validation</td> <td>2587</td> <td>2370</td> </tr> <tr> <td>Testing</td> <td>1572</td> <td>1449</td> </tr> </tbody> </table>   Reference: [1] A. Savva et al., "ICARUS: Automatic Autonomous Power Infrastructure Inspection with UAVs," 2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021, pp. 918-926, doi: 10.1109/ICUAS51884.2021.9476742. **NOTE** If you use this dataset in your research/publication please cite us using the following : <blockquote> Antonis Savva, Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2023). Aerial Power Infrastructure Detection Dataset (2.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7781388 </blockquote>

opencc-by-4.0Jun 2023View details →

Trajectory-Aware Rate Adaptation for Aerial Networks Simulation Results

Introduction Even though the concept of ubiquitous wireless connectivity is becoming a reality, there are scenarios where wireless communications coverage is insufficient or does not exist. Considering natural and man-made disaster scenarios, communications infrastructures may be damaged and become unavailable. In temporary crowded events, the existing infrastructure may not have been designed to cope with the additional traffic demand, resulting in overload. In maritime scenarios, environmental monitoring activities using autonomous vehicles will take place in offshore zones, typically not in range of existing onshore communications infrastructures. Flying networks, composed of Unmanned Aerial Vehicles (UAV), are emerging as a flexible and cost-effective solution to provide on-demand wireless connectivity in such scenarios. UAVs have the possibility to operate virtually everywhere, and the growing payload capacity makes them suitable platforms to carry wireless communications hardware, playing the role of mobile base stations, access points or relay nodes. A flying network may typically be composed of a fleet of UAVs, organized in a multi-tier topology with so-called Flying Edge Nodes (FENs) and Flying Gateways (FGWs) <a href="https://doi.org/10.1016/j.adhoc.2022.103000">[1]</a>. FENs can play the role of Flying Access Points that provide the access network to the users on the ground, or the role of Flying Sensor Nodes that can perform video surveillance missions. The FENs forward the traffic to the FGWs, that act as relay nodes and are responsible for forwarding the traffic to/from the backhaul (BKH) network and ultimately to/from the Internet. The flying network concept brings up new challenges. The flying nodes need to be properly positioned and their wireless link configuration dynamically adjusted in order to ensure the Quality of Service (QoS) expected by the end users. In addition, these scenarios are typically highly unpredictable due to the varying locations as well as the concentration/dispersion of end-users and their movements regarding direction and velocity - e.g., vehicles or pedestrians. Therefore, a static wireless link configuration and UAV positioning are not adequate. State of the art work has been mainly focused on the optimal positioning of the flying nodes, having most of the wireless link parameters statically configured with default values. The Rate Adaptation challenge is well-known in fixed or low mobility IEEE 802.11 networks, and Minstrel High Throughput (HT) <a href="https://lwn.net/Articles/376765">[2]</a> is the default Wi-Fi rate adaptation algorithm used in the Linux kernel since the IEEE 802.11n version. However, few works propose solutions designed to consider the characteristics of other communications environments, such as flying and vehicular networks <a href="https://doi.org/10.1007/s11276-020-02295-2">[3]</a>. To the best of our knowledge, solutions that use the node trajectory information to predict the wireless channel conditions and perform rate adaptation are yet to be developed. The main contribution of this paper is the Trajectory-Aware Rate Adaptation (TARA) algorithm. TARA takes advantage of knowing the trajectory of all nodes in the flying network to estimate future changes in the wireless link quality and perform rate adaptation accordingly. The network performance improvement achieved with TARA was evaluated using ns-3 <a href="https://doi.org/10.1007/978-3-642-12331-3_2">[4]</a>. The simulation results presented in this dataset show significant throughput gains when compared with conventional rate adaptation algorithms. Folder Organization The following dataset presents the results of the TARA Paper, organized in different folders for each Rate Adaptation Algorithm, as well as the random seeds that were used to obtain such results: Naming Convention: <ul> <li>Rate Adaptation Algorithm  <ul> <li>tara – Trajectory-Aware Rate Adaptation</li> <li>min – MinstrelHTWifiManager</li> <li>id – IdealWifiManager</li> </ul> </li> </ul> Folder Content: <ul> <li>distances.csv - Distances between nodes <ul> <li>Column 1 – Simulation Time (seconds)</li> <li>Column 2 – Distance between BKH and FGW (meters)</li> <li>Column 3 – Distance between FEN and FGW (meters)</li> </ul> </li> <li>positions.csv - Current 3D position of nodes <ul> <li>Column 1 – Simulation Time (seconds)</li> <li>Column 2 – BKH x (meters)</li> <li>Column 3 – BKH y (meters)</li> <li>Column 4 – BKH z (meters)</li> <li>Column 5 – FEN x (meters)</li> <li>Column 6 – FEN y (meters)</li> <li>Column 7 – FEN z (meters)</li> <li>Column 8 – FGW x (meters)</li> <li>Column 9 – FGW y (meters)</li> <li>Column 10 – FGW z (meters)</li> </ul> </li> <li>throughput.csv - Link Specific Throughput, at MAC layer level <ul> <li>Column 1 – Simulation Time (seconds)</li> <li>Column 2 – Relay Link (BKH - FGW), Throughput measured in BKH (Mbit/second)</li> <li>Column 3 – Access Link (FEN - FGW), Throughput measured in FEN (Mbit/second)</li> <li>Column 4 – Relay Link (BKH - FGW), Throughput measured in FGW (Mbit/second)</li> <li>Column 5 – Access Link (FEN - FGW), Throughput measured in FGW (Mbit/second)</li> </ul> </li> </ul>

edi48/100

Aerial Photographs of the KBS LTER and Environs at the Kellogg Biological Station, Hickory Corners, MI (1938 to 2020)

Dataset Abstract Aerial photography is considered an important management tool in agriculture. Aerial photography allows researchers to detect spatial variability and understand the causes of the variability such as planter skips, drought stress, weeds and water erosion. In agricultural research it allows researchers to differentiate healthy vegetation from unhealthy and access plant biomass and moisture levels. The photographs are also useful to document trends and changes in the landscape. original data source http://lter.kbs.msu.edu/datasets/44

openCustomMar 2022View details →

opencc-by-4.0Dec 2020View details →

University of Nebraska-Lincoln Unmanned Aerial System observations from LAPSE-RATE

This dataset contains thermodynamic (pressure, temperature, humidity) measurements from the University of Nebraska-Lincoln unmanned aerial system multirotors during the LAPSE-RATE campaign from 14--19 July 2018. This dataset contains 171 files from two multirotors. File names are coded as per the LAPSE-RATE community standard naming agreement. See README_v1.txt for more information about the naming convention and other details.

opencc-by-4.0Nov 2021View details →

Passive Perching with Energy Storage for Winged Aerial Robots Dataset

This dataset corresponds to the publication: "Passive Perching with Energy Storage for Winged Aerial Robots" W. Stewart, L. Guarino, Y. Piskarev, and D. Floreano. Advanced Intelligent Systems, <a href="http://doi.org/10.1002/aisy.202100150">http://doi.org/10.1002/aisy.202100150</a>

opencc-by-4.0Mar 2022View details →

Roundabout Aerial Images for Vehicle Detection

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.   <table align="center"> <tbody> <tr> <td> Roundabout (scenes) </td> <td> Frames </td> <td> Car </td> <td> Truck </td> <td> Cycle </td> <td> Bus </td> <td> Empty </td> </tr> <tr> <td> 1 (00001) </td> <td> 1,996 </td> <td> 34,558 </td> <td> 0 </td> <td> 4229 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 2 (00002) </td> <td> 514 </td> <td> 743 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 157 </td> </tr> <tr> <td> 3 (00003-00017) </td> <td> 1,795 </td> <td> 4822 </td> <td> 58 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 4 (00018-00033) </td> <td> 1,027 </td> <td> 6615 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> <td> 0 </td> </tr> <tr> <td> 5 (00034-00049) </td> <td> 1,261 </td> <td> 2248 </td> <td> 0 </td> <td> 550 </td> <td> 0 </td> <td> 81 </td> </tr> <tr> <td> 6 (00050-00052) </td> <td> 5,501 </td> <td> 180,342 </td> <td> 1420 </td> <td> 120 </td> <td> 1376 </td> <td> 0 </td> </tr> <tr> <td> 7 (00053) </td> <td> 2,036 </td> <td> 5,789 </td> <td> 562 </td> <td> 0 </td> <td> 226 </td> <td> 92 </td> </tr> <tr> <td> 8 (00054) </td> <td> 1,344 </td> <td> 1,733 </td> <td> 222 </td> <td> 0 </td> <td> 150 </td> <td> 222 </td> </tr> <tr> <td> Total </td> <td> 15,474 </td> <td> 236,850 </td> <td> 2,262 </td> <td> 4,899 </td> <td> 1,752 </td> <td> 552 </td> </tr> <tr> <td> Data augmentation </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> <td> x4 </td> </tr> <tr> <td> Total </td> <td> 61,896 </td> <td> 947,400 </td> <td> 9048 </td> <td> 19,596 </td> <td> 7,008 </td> <td> 2,208 </td> </tr> </tbody> </table>

opencc-by-4.0Jul 2022View details →

Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.

This database provides as polygons the contours of 900 juniper shrubs (Juniperus communis L. and Juniperus sabina L.) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation).

opencc-by-4.0Mar 2020View details →

Drone aerial imagery of a small island in Kobbefjord (SW Greenland) acquired during Mission Arctic 2017

<code>Aerial images of a small island in Kobbefjord (SW Greenland) were acquired on 5 July 2017 using a DJI Phantom 3 Standard drone during the <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.665582/full">Mission Arctic citizen science expedition</a>. Images were processed in Agisoft Metashape. A digital elevation model (DEM) and an orthomosaic were exported at 5 cm resolution. For details, see the readme and processing report that accompanies this dataset. </code>

Drone aerial imagery of a headland in Nuup Kangerlua (Godthåbsfjord, SW Greenland) acquired during Mission Arctic 2017

Aerial images of a headland in Nuup Kangerlua (Godthåbsfjord, Greenland) were acquired on 11 July 2017 using a DJI Phantom 3 Standard drone during the <a href="https://www.frontiersin.org/articles/10.3389/fmars.2021.665582/full">Mission Arctic citizen science expedition</a>. Images were processed in Agisoft Metashape. A digital elevation model (DEM) and an orthomosaic were exported at 2.5 cm resolution. A 5 cm resolution orthomosaic is also included. For details, see the readme and processing report that accompanies this dataset.

opencc-by-4.0Mar 2017View details →

V4RL Aerial Inspection Dataset

This dataset contains visual and inertial sequences recorded from the ground and the air (using a small rotorcraft) while moving around a building. This data is captured with a hardware-synchronised sensor and ground-truth of the scene has been captured using a laser scanner. Users of this dataset are asked to cite the following paper, where this dataset was introduced: Lucas Teixeira and Margarita Chli, "Real-Time Mesh-based Scene Estimation for Aerial Inspection", in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

opencc-by-4.0Jul 2024View details →

Typical Village UAV Aerial Photography Dataset in northeastern Tibetan Plateau (2022)

This dataset was collected during a field survey in the Hehuang Valley, located in the northeastern Tibetan Plateau, in July and August 2022. Using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c camera, over 4,600 aerial photographs were captured from 55 typical villages across the region. These images were processed into high-resolution orthophotos using Agisoft PhotoScan 1.25 software, resulting in ultra-high-precision orthophoto data for the 55 villages. The "Village Information" section provides detailed information on each village, including its full name, abbreviation, latitude and longitude coordinates, and elevation. This dataset accurately reflects the overall situation, spatial patterns, and surrounding environment of the typical villages, offering a high-resolution data source for spatial structure analysis, land use mapping, and correction tasks.