These inland electronic Navigational charts (IENCs) were developed from available data used in maintenance of Navigation channels. Users of these IENCs should be aware that some features and attribute information could have significant inaccuracies due to changing waterway conditions, inaccurate source data, or approximations introduced during chart compilation. Caution is urged in use of these IENCs or derived products for Navigational planning or operation, or any decisions pertaining to or affecting safety of vessel operation. These initial IENCs are not to be used as replacements for official government chart books, as required in the U.S. Code of Federal Regulations.

This feature layer, Aerial Photos - September 2020, provides aerial imagery of multiple locations along North Mountain in Botetourt County, VA. These photos depict the base-line conditions of the proposed Rocky Forge Wind project by Apex Clean Energy, and are associated with Pipeline Air Force.Source and date:These images were sourced from Pipeline Air Force. Accessed September of 2020.Purpose:Pipeline Air Force is a group of volunteers working to take photos from the sky to provide information on land conditions. They are able to produce imagery to which the general public does not typically have access. Processing:ABRA linked the JPG photos to their associated point location popups in ArcGIS Online. Symbolization:The following symbolization is how it appears in the Rocky Forge Wind online map provided by ABRA.April Aerial Photo: small plane symbols

This dataset is a comprehensive inventory of Alaskan buildings, storage tanks, and roads that were: (1) detected from 0.5 meter resolution satellite imagery of communities (acquired between 2018-2023) and (2) supplemented by OpenStreetMap data. We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery across the Arctic region. Shapefiles beginning with "HABITAT_AK" contain only the post-processed deep learning predictions. Shapefiles beginning with "HABITAT_OSM" contain the post-processed deep learning predictions supplemented by OpenStreetMap data. The HABITAT pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover. Finally, we selected buildings and roads from the OpenStreetMap Alaska dataset (downloaded in June 2024 from https://download.geofabrik.de/) that do not intersect with any deep learning predictions to generate a merged OSM and HABITAT infrastructure dataset. This merged product comprises a total building footprint of 53 million square meters and a road network of 63,744 km across the state of Alaska.

Vehicle detection is a very important aspect of computer vision application to aerial and satellite imagery, facilitating activities such as instance counting, velocity estimation, traffic predictions, etc. The feasibility of accurate vehicle detection often depends on limited training datasets, requiring a lot of manual work in collection and annotation tasks. Furthermore, there are no known publicly available datasets. Our aim was to construct a pipeline for synthetic dataset generation from aerial imagery and 3D models in Blender software. The dataset generation pipeline consists of seven steps and results in a wished number of images with bounding boxes in YOLO and coco formats. This synthetic dataset has been produced following the steps described in this pipeline. It consists of 5000 2048x2048 images with cars inserted into the roads and highways at the images without cars from all over the world. We believe that this dataset and the respective pipeline might be of great importance for vehicle detection, facilitating the customizability of the models to specific needs and context.

The aerial imaging and mapping market is experiencing robust growth, driven by increasing demand across diverse sectors. Government agencies leverage this technology for infrastructure monitoring, urban planning, and disaster response. The military and defense sectors utilize it for surveillance, reconnaissance, and target acquisition. The energy sector employs aerial imaging for pipeline inspections, renewable energy site assessments, and resource exploration. Precision agriculture benefits from detailed crop analysis and yield optimization, while civil engineering uses it for project planning, construction monitoring, and asset management. Commercial enterprises are increasingly adopting aerial imaging for real estate assessments, construction progress tracking, and marketing purposes. The market is segmented by platform type, with unmanned aerial vehicles (UAVs or drones) experiencing rapid adoption due to their cost-effectiveness and ease of deployment. Helicopters and fixed-wing aircraft continue to play crucial roles in large-scale projects requiring longer flight durations and heavier payloads. Technological advancements, including higher-resolution sensors, improved data processing capabilities, and AI-powered analytics, are fueling market expansion. Despite these positive trends, challenges remain. High initial investment costs associated with equipment and specialized software can act as a barrier to entry for smaller companies. Data privacy and security concerns necessitate robust regulatory frameworks and ethical considerations. Furthermore, weather dependency and airspace regulations can limit operational efficiency. However, the overall market outlook remains optimistic, projecting a significant expansion over the forecast period (2025-2033). The increasing availability of user-friendly software, coupled with falling hardware costs, is expected to further democratize access to aerial imaging and mapping technologies, driving wider adoption across diverse applications. Specific regional growth will vary, with North America and Europe anticipated to maintain substantial market share due to robust technological advancements and high adoption rates.

We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery in Arctic communities. The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT. The pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada). From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover.

A line feature class representing above ground pipes within the municipal boundaries of the City of London as identified through aerial imagery.

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since stakeholders often lack quality data about these installations. Overhead imagery is increasingly used to improve the knowledge of distributed PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide ground truth annotations and associated installation metadata for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.

This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", currently under review. These complete records consist of RGB overhead imagery, segmentation masks, and characteristics of PV installations. The data records are organized as follows:

• bdappv/ Root data folder
  • google / ign: One folder for each campaign
    • img/: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.
    • mask/: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.
• metadata.csv The .csv file with the characteristics of the installations.

The global satellite imagery and image processing services market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are providing higher-resolution imagery with improved accuracy and faster processing times, enabling more detailed analysis for various applications. Secondly, the rising adoption of cloud-based platforms for image processing and analytics is streamlining workflows and reducing costs for users. This is particularly crucial for smaller businesses and organizations that previously lacked access to sophisticated image processing capabilities. Thirdly, the growing need for precise geographical information across diverse sectors, including environmental monitoring, precision agriculture, urban planning, and disaster response, fuels market demand. The defense and security sector remains a significant contributor, with increasing reliance on satellite imagery for intelligence gathering and surveillance. Market segmentation reveals significant opportunities within specific application areas. The environmental sector, utilizing satellite imagery for deforestation monitoring, climate change analysis, and pollution detection, is a rapidly growing segment. Similarly, the energy and power sector leverages satellite imagery for pipeline monitoring, renewable energy resource assessment, and infrastructure management. Within image processing types, the demand for advanced data analytics is soaring, with growing adoption of artificial intelligence and machine learning for automated feature extraction and predictive analysis. While regulatory hurdles and the high initial investment cost of satellite technologies pose some challenges, the overall market outlook remains positive, driven by technological advancements, increasing data accessibility, and rising demand for location-based intelligence. Competition is intensifying amongst established players and new entrants, leading to innovation and affordability in the market.

The global aerial survey services market, valued at $16.09 billion in 2025, is projected to experience robust growth, exhibiting a compound annual growth rate (CAGR) of 4.0% from 2025 to 2033. This expansion is driven by several key factors. Increasing demand for precise and efficient data acquisition across diverse sectors, including construction, agriculture, and environmental monitoring, fuels market growth. Technological advancements in drone technology, sensor capabilities, and data processing software are enabling higher resolution imagery, faster processing times, and more detailed analysis, further bolstering market adoption. The rising need for infrastructure development, particularly in emerging economies, coupled with stricter environmental regulations mandating comprehensive surveys, significantly contributes to market expansion. Furthermore, the integration of AI and machine learning in data analysis enhances the accuracy and efficiency of aerial surveys, further driving demand. Segmentation analysis reveals a strong presence across various applications. Forestry and agriculture benefit significantly from aerial surveys for precision farming and land management. Similarly, the construction industry leverages aerial data for site planning, progress monitoring, and infrastructure inspections. The energy sector utilizes aerial surveys for pipeline monitoring, power line inspections, and resource exploration. Environmental studies rely heavily on this technology for monitoring deforestation, pollution levels, and ecosystem changes. While aircraft remain the dominant platform, the increasing adoption of drones and satellites indicates a shifting technological landscape. Competitive analysis indicates a diverse range of companies operating in this market, representing a blend of established players and innovative startups, indicative of a dynamic and competitive market environment. The geographical distribution of market activity mirrors global infrastructure development and economic activity, with North America and Europe currently holding significant market share, while Asia-Pacific presents a substantial growth opportunity.

This dataset is part of a project aimed at collecting high-resolution satellite imagery of the Gaza Strip before and after the recent conflict. The images were retrieved using Sentinel Hub API and Planet.com API, covering weekly snapshots from January 2023 to the present.

With over 3,500 images, each accompanied by a metadata JSON file, this dataset enables researchers, analysts, and humanitarian organizations to study urban damage assessment, environmental changes, and infrastructure impact.

Key Features:

• Weekly satellite images from multiple sources (Sentinel-2, Landsat-8, PlanetScope)
• Configurable grid-based coverage of the entire Gaza Strip
• Each image includes metadata (timestamps, coordinates, satellite source, etc.)
• Supports before & after visualizations for damage assessment
• Open-source processing pipeline available on GitHub

GitHub Repository (Source Code & Data Collection Pipeline):

https://github.com/AbdooMohamedd/satellite-imagery-app

A high-resolution, high vertical accuracy Digital Surface Model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set (https://ti.arc.nasa.gov/tech/asr/groups/intelligent-robotics/ngt/stereo/). The NASA Ames Stereo Pipeline (ASP) is a suite of free and open source automated geodesy and stereogrammetry tools designed for processing stereo imagery captured from satellites (around Earth and other planets), robotic rovers, aerial cameras, and historical imagery, with and without accurate camera pose information. The methodology used by the ASP software is similar with structure-from-motion (SfM) methodology using stereo triangulation combining spacecraft ephemeris/attitude information, sensor model, and disparity map information obtained by feature matching procedures. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical root mean square error (RMSE) of 0.78 m. The horizontal accuracy is 1 cell (2 m) and the vertical resolution is centimeters. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short processing time making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.

The use of high-resolution remotely sensed imagery can be an effective way to obtain quantitative measurements of rock-avalanche volumes and geometries in remote glaciated areas, both of which are important for an improved understanding of rock-avalanche characteristics and processes. We utilized the availability of high-resolution (~0.5 m) WorldView satellite stereo imagery to derive digital elevation data in a 100 km2 area around the 28 June 2016 Lamplugh rock avalanche in Glacier Bay National Park and Preserve, Alaska. We used NASA Ames Stereo Pipeline, an open-source software package available from NASA, to produce one pre- and four post-event digital elevation models (DEMs) of the area surrounding the Lamplugh rock avalanche. This data release includes five raster elevation datasets (2-m resolution) in GeoTIFF format that have been orthrectified to the Universal Transverse Mercator (UTM) coordinate system (zone 7N). Elevations are measured in reference to the World Geodetic System 1984 (WGS84) ellipsoid. Because the study area is remote and difficult to access, ground control was not available to assess the absolute accuracy of DEMs. The DEMs have not been precisely co-registered. Data contained in this release include a pre-event DEM from 15 June 2016, and post-event DEMs from 16 July 2016, 27 August 2016, 27 September 2016, and 28 September 2016. The filenames for these DEMs are 20160615_LamplughDEM.tif, 20160716_LamplughDEM.tif, 20160827_LamplughDEM.tif, 20160927_LamplughDEM.tif, and 20160928_LamplughDEM.tif, respectively. We also provide a CSV file (Lamplugh_DEM_Image_Notes.csv) that contains the acquisition date, satellite platform, image identification number, resolution, off-nadir angle, and notes on image quality for each stereo pair used to generate DEMs.

Here you find the satellite-derived maps (PNGs) of predicted locations of floating macroplastic aggregations between 2019 and 2024, in the coastal region of the Motagua River plume (Honduras), developed under the H2020 LABPLAS project. The maps are classified using the POS2IDON tool and two specific machine learning models (the UNET and XGBOOST models) and the true-color (RGB) images. The predicted locations of floating macroplastic aggregations are marked with large red dots in the classified maps. The classification maps were created with POS2IDON pipeline, a tool to detect suspected locations of floating marine debris in Sentinel- 2 satellite imagery using machine learning. POS2IDON includes modules for data acquisition, pre-processing, and pixel-based classification using different machine learning models (e.g. Random Forest, XGBoost, Unet). The outputs of POS2IDON include classification maps for all the available Sentinel-2 imagery of a given region of interest and temporal period, specified by the user. Using POS2IDON we performed a long-term analysis in the region of the Motagua River plume (Honduras), which is infamous for its plastic pollution, largely due to substantial riverine inputs of plastic from Motagua river. This river is known for its "trash tsunamis" and the OceanCleanUp has installed the “Interceptor Barricade” in an upstream location. Long-term synoptic assessments of plastic pollution are the primary goal, and the Honduras Gulf represents an outstanding example of a region to test methods. Confidence in results can be taken especially in the Honduras Gulf region since it is known for recurrent and large aggregations of floating plastics in the coastal ocean, and where the models are trained. POS2IDON was applied in the Sentinel-2 archive between 2019 and 2024. After running POS2IDON, classification images (for UNET and XGBOOST models) were obtained. Subsequently, to decrease the number of potential false positives, automated post-processing procedures were applied, including the buffering of the clouds (from cloud mask and cloud class) and the removal of isolated MD pixels. This resulted in a total of 438 classified & post-processed images (i.e. 1 image every ~5 days), which were then used for long-term analysis, and provided in this dataset.

Here you find the satellite-derived maps (PNGs) of predicted locations of floating macroplastic aggregations between 2016 and 2024, for three areas of interest (AOI) in the German Bight, developed under the H2020 LABPLAS project. The maps are classified using the POS2IDON tool and two specific machine learning models (the UNET and XGBOOST models) and the true-color (RGB) images. The predicted locations of floating macroplastic aggregations are marked with large red dots in the classified maps. The classification maps were created with POS2IDON pipeline, a tool to detect suspected locations of floating marine debris in Sentinel- 2 satellite imagery using machine learning. POS2IDON includes modules for data acquisition, pre-processing, and pixel-based classification using different machine learning models (e.g. Random Forest, XGBoost, Unet). The outputs of POS2IDON include classification maps for all the available Sentinel-2 imagery of a given region of interest and temporal period, specified by the user. Using POS2IDON we performed a long-term analysis in three AOIs in the German Bight (North Sea), which is part of the LabPlas Case Study 1 area. To cope with potential false positives and retain the more relevant spatial and temporal patterns, POS2IDON was applied in the entire Sentinel-2 archive resulting in a massive processing of nearly 9 years of data (2016-2024). After running POS2IDON, classification images (for UNET and XGBOOST models) were obtained in the three AOIs. Subsequently, to decrease the number of potential false positives, automated post-processing procedures were applied, including the buffering of the clouds (from cloud mask and cloud class) and the removal of isolated MD pixels. Furthermore, images contaminated by clouds were automatically excluded using cloud cover percentage thresholds, and through visual analysis several images affected by sunglint and whitecaps were manually excluded. This resulted in a total of 95, 97 and 92 images for AO1, AO2 and AO3, respectively, that were used for long-term analysis and provided in this dataset.

Commercial Fishing Regulations in this dataset include, but are not limited to: Fisheries (Auckland and Kermadecs Commercial Fishing) Regulations 1986, Fisheries (Challenger Area Commercial Fishing) Regulations 1986, Fisheries (Central Area Commercial Fishing) Regulations 1986, Fisheries (South-East Area Commercial Fishing) Regulations 1986, Fisheries (Southland and Sub-Antarctic Areas Commercial Fishing) Regulations 1986, Fisheries (Commercial Fishing) Regulations 2001 and the Submarine Cables and Pipelines protection Act 1996. Current and historical copies of these regulations can be found online at: www.legislation.govt.nzAlso included in this layer are Fisheries (Benthic Protection Areas) Regulations 2007, Fishery Notices (including beach cast seaweed and temporary closures), Marine Reserves, Marine Mammal Protection Notices, Submarine Cables and Pipelines Protections and other restrictions.Method of creation: The geometries of the features were sourced from the area descriptions provided within legislation or from another agency. The polygons were created by various methods, such as, but not limited to: using gazetted coordinates, snapping polygon vertices to LINZ Topo50 Coastline, relating to Aerial Imagery, appending polygons from other datasets such as the LINZ New Zealand Exclusive Economic Zone layer.Please note: Many features in this dataset contain general inland boundaries that are not part of the defined area. Features with a value of 1 in the ‘CoastlineRemoval’ field contain general inland boundaries. This is for the purposes of reducing vertices (to improve performance) and to future proof the data from changes to the coastline (users can erase the latest coastline data from the layer). For analysis, it is advised to erase the coastline area from features with general inland boundaries, such as the LINZ Topo 1:50k Coastline layer. The following fields within the attribute table may include helpful information related to how the geometries were created: MappingNote, CoastlineRemoval, SpecialConditions, AreaDescriptionURL and Keywords.

This table enumerates the selected photographs from an initial pool of over 70 images, filtered based on criteria detailed in the discussion of ‘the appropriateness of ground photos’ (see Results and discussion section).

Large-scale and high-velocity monitoring of perennial plants, such as date palms, is essential for sustainable agriculture and early warning of biotic/abiotic stresses. This paper builds an automated detection pipeline for large-scale date palm tree identifi?cation from open-source satellite imagery, using 0.3-meter resolution Google Earth images over varied UAE sites. We propose a customized deep learning architecture derived from YOLOv11, which was trained on a carefully curated set of 132 images containing over 10,000 annotated objects. Our pipeline utilizes SAS Planet for geo?tagged image procurement, Roboflow for multistage data processing (polygon-based annotation and heavy augmentation using horizontal/vertical flipping, ±23° rota?tion,±15% brightness adjustment, 2.56% noise injection, and stringent validation protocols. The YOLOv11 architecture, which utilizes C3K2 blocks for lightweight feature extraction, SPFF modules for multiscale spatial processing, and C2PSA attention mechanisms, achieved precision of 96% on the test data, surpassing re?cent models (YOLOv8: 93%, YOLO-NAS: 93%). This paper builds an open source, scalable pipeline for palm detection, directly transferable to resource optimization in UAE date palm agriculture while offering a reproducible framework for global arid-region agriculture.

Evaluation of the the Segment Anything Model (SAM) for penguin colony segmentation using mean intersection over union (mIoU), difference in perimeter to area ratio (PAR), area error, and accuracy (i.e. panels a-c in Figs 3 and 4 vs. ground truth). 95% confidence intervals are shown. An up (down) arrow indicates a measure where a larger (smaller) number is preferred.

We use the Devil Island dataset to conduct a sensitivity analysis for the number of pixel prompts needed using mean intersection over union (mIoU), difference in perimeter to area ratio (PAR), and area error. An up (down) arrow indicates a measure where a larger (smaller) number is preferred.