This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:

Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where:

[location] is the place of data collection (e.g., Cocora, Vinicucna)

[year] is the year of data collection (e.g., 2023)

[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade

[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)

Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).

Structure-from-motio n processing was performed in Agisoft Metashape (https://www.agisoft.com/, Agisoft, 2023). Mapping was performed in ArcGIS Pro (https://www.esri.com/en-us/arcgis/about-arcgis/overview, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).

Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. International Journal of Applied Earth Observations and Geoinformation, 103474. doi: https://doi.org/10.1016/j.jag.2023.103474

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images during search and rescue purposes. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model can detect humans by looking at drone imagery and can draw bounding boxes around the location. This model is trained on IPSAR and SARD datasets where humans are on macadam roads, in quarries, low and high grass, forest shade, and Mediterranean and Sub-Mediterranean landscapes. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing the time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humans.Applicable geographiesThe model is expected to work well in Mediterranean and Sub-Mediterranean landscapes but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 82.2 percent for human class.Training dataThis model is trained on search and rescue dataset provided by IPSAR and SARD.LimitationsThis model has a tendency to maximize detection of humans and errors towards producing false positives in rocky areas.Sample resultsHere are a few results from the model.

The Drone Capabilities story map is an introduction to the technology, capabilities and potential data sets involved with the use of drones and UAS. It is intended to be used for education, ideation and outreach to the departments and staff of Waukesha County in an effort to increase awareness and understanding of drones. The story map includes drone images, video, 3D renderings and technical descriptions to give an overview of the capabilities and limitations of the Mavic Pro drone.

Discover the booming Drone GIS Mapping market! This comprehensive analysis reveals market size, CAGR, key trends, and regional insights (North America, Europe, Asia-Pacific), highlighting growth drivers and challenges from 2019-2033. Explore applications in agriculture, construction, and energy.

Drone products captured by CDFW staff for Hope Valley Wildlife Area.

Flight Areas flown by STRI Drones Program

This deep learning model is used to detect trees in low-resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.

This deep learning model is based on MaskRCNN and has been trained on data from the DM Dataset preprocessed and collected by the IST Team.

There is no need of high-resolution imagery you can perform all your analysis on low resolution imagery by detecting the trees with the accuracy of 75% and finetune the model to increase your performance and train on your own data.

Licensing requirements ArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Online

Using the model Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.

Input 3-band low-resolution (70 cm) satellite imagery.

Output Feature class containing detected trees

Applicable geographies The model is expected to work well in the U.A.E.

Model architecture This model is based upon the MaskRCNN python package and uses the Resnet-152 model architecture implemented in pytorch.

Training data This model has been trained on the Satellite Imagery created and Labelled by the team and validated on the different locations with more diverse locations.

Accuracy metrics This model has an average precision score of 0.45.

Sample results Here are a few results from the model.

This map shows locations where STRI drones have flown. For each of these polygons, we have orthophotos, digital surface model (DSM) and cloud points. At the Smithsonian Tropical Research Institute (STRI) Panama, we have DJI Phantom 4 pro, Sense Fly eBee, 3DR Solo among other drones ready to flight, depending of the area, coverage and parts availability.If you need access to any of the byproducts, please, send us an email requesting the data.

rond point CD44 Drone

Chateau de la Colaissière

If you have a drone or are thinking abut getting one to fly with your students or for your research, there are several things that you should consider. In this article we look at what you need to do to ensure you are safe and comply with the UK regulations.

As we soar into the world of drones, we extend a warm welcome from our team in beautiful Northeastern Pennsylvania.Join us as we uncover the Northeastern Pennsylvania Alliance's innovative use of drone technology. We'll explore the 'why' and 'how' behind their UAS Program, revealing its transformative potential and outlining pathways for you to engage with this rapidly evolving field that is shaping the future and how you, too, can take part in this evolving technology. So buckle up and get ready for takeoff!

Dataset for Exercise 2 (Part II)See: https://drive.google.com/file/d/1IipANOHIUBetKj-LC6GJAsXGnrDLyN4e/view?usp=sharingDataset contains an orthomosaic collected by a DJI Inspire 1 drone with a Micasense (RedEdge) sensor.

Thumbnail for RPAS (Drone) Program Card on WTD GIS Hub Site

This is an example of drone corridors over the rail line in Sonoma County, CALidar downloaded from:http://sonomavegmap.org/data-downloads/Buildings and corridors were generated by Esri from the above LAS data.

Flight paths of drone surveys used to capture imagery and video for the June 5, 2023, Peerless, SK downburst. Ground survey conducted June 6 & 7, 2023. DJI Mavic 2 Pro performed 3 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery.

This map will illustrate the flight path of our drone and the data it collects.

Flight paths of drone surveys used to capture imagery and video for the June 20, 2023, East Boundary Lake, MB tornado. Ground survey conducted June 22, 2023. DJI Air 2S performed 9 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery.View Event Summary Map Here

Flight paths of drone surveys used to capture imagery by the NHP for the August 20, 2025 Brooks, AB downburst. Ground survey conducted August 22-24, 2025. Note that additional drone photos were also captured by the NTP. DJI Mavic 3E performed 3 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery.View Survey Summary Map

Aerial imagery captured by an Inspire 1 drone with photos processed by DroneDeploy and corrected using Ground Control Points.