Aerial image dataset

Aerial Images. The dataset consists of 4 aerial images in colour (Figures 2-5), scanned with 14 microns, the format is Tiled Tiff , each image is about 16500 x 16400 pixels, that means that storage of each image needs about 840 MB Awesome Satellite Imagery Datasets . List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other) The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building footprints. The rest of the dataset is used only for evaluation with a hidden ground truth. The dataset was constructed by combining public. Welcome to NASA Earth Observations, where you can browse and download imagery of satellite data from NASAs Earth Observing System. Over 50 different global datasets are represented with daily, weekly, and monthly snapshots, and images are available in a variety of formats

Aerial Bold (A Font Made Entirely of Satellite Imagery of

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) Dataset. Paper. You can find the article related to this code here at Elsevier or You can find the preprint from the Arxiv website. Dataset presented dataset. Aerial Visual Datasets. With the rapid development of the commercial UAV, more and more aerial visual datasets have been constructed to facilitate the research of aerial vision tasks. DOTA [39] and NWPU VHR-10 [9] are the datasets collected for object detection in aerial images which are taken by UAVs from a relatively high. The Dataset. For the object detection portion of the project, we used the Cars Overhead With Context (COWC) dataset, which is provided by the Lawrence Livermore National Laboratory. It features aerial imagery taken in six distinct locations: Toronto, Canada. Selwyn, New Zealand. Potsdam and Vaihingen*, Germany. Columbus (Ohio)* and Utah, USA

EarthExplorer. Search Criteria. Data Sets. Additional Criteria. Results. 1. Enter Search Criteria. To narrow your search area: type in an address or place name, enter coordinates or click the map to define your search area (for advanced map tools, view the help documentation ), and/or choose a date range. Geocoder Dataset. We construct a large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images. This new dataset, which is named as Gaofen Image Dataset (GID), has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. GID consists of two parts: a large-scale. They've developed the INPE Image Catalog which is like a library to download free satellite imagery. A big part of this catalog is satellite imagery from CBERS. CBERS: The partnership between Brazil and China has their own joint mission. The key data set in this catalog is China-Brazil Earth Resources (CBERS 2) Image augmentations should be selected based on the domain problem one is tackling. Aerial data, specifically, often shares a number of characteristics that enable similar selection of image augmentation to be performed. Aerial data is any data captured from above looking down. Commonly, this includes drone and satellite imagery

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Aerial Data and Imagery - High-Resolution Aerial Dat

ISPRS Data sets: Zurich Hoeng

  1. In recent years, satellite image datasets have become available to anyone with a computer and an internet connection. The quality, quantity, and precision of these datasets is continuously improving, and there are many free and commercial platforms at your disposal to acquire satellite images. On top of that, the prices of acquiring the images.
  2. A dataset of large-scale aerial images produced by Intelinair, a spinout from the University of Illinois at Urbana-Champaign, aims to give farmers visibility into the conditions of their fields
  3. The Massachusetts Roads Dataset consists of 1171 aerial images of the state of Massachusetts. Each image is 1500×1500 pixels in size, covering an area of 2.25 square kilometers. We randomly split the data into a training set of 1108 images, a validation set of 14 images and a test set of 49 images
  4. Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface
  5. Author(s): Abhishek Annamraju Computer VisionA list of object detection and image segmentation datasets (With colab notebooks for training and inference) to explore and experiment with different algorithms on!Free to use Image. CreditsComputer Vision is such a fast-paced field that everyday loads
  6. Image Source and Usage License The DOTA images are collected from the Google Earth, GF-2 and JL-1 satellite provided by the China Centre for Resources Satellite Data and Application, and aerial images provided by CycloMedia B.V. DOTA consists of RGB images and grayscale images
  7. Automated object detection in high-resolution aerial imagery can provide valuable information in fields ranging from urban planning and operations to economic research, however, automating the process of analyzing aerial imagery requires training data for machine learning algorithm development. This dataset seeks to meet that need. For 25 locations across 9 U.S. cities, this dataset provides.
Montana Satellite Poster Map — aerial views, from space

To solve this p r oblem, we'll try to detect cars and swimming pools in RGB chips of 224x224 pixels of aerial imagery. The training dataset had 3748 images with bounding box annotations and labels in PASCAL VOC format. This problem along with the dataset was posted by ESRI on HackerEarth as the ESRI Data Science Challenge 2019 The aerial pile burn detection dataset consists of different repositories. The first one is a raw video recorded using the Zenmuse X4S camera. The format of this file is MP4. The duration of the video is 966 seconds with a Frame Per Second (FPS) of 29. The size of this repository is 1.2 GB isting aerial image datasets [41, 18, 16, 25] share in com-mon several shortcomings: insufficient data and classes, lack of detailed annotations, as well as low image resolu-tion. Moreover, their complexity is inadequate to be con-sidered as a reflection of the real world. Datasets like TAS [9], VEDAI [25], COWC [21 dataset in aerial images has unique challenges compared to regular image datasets (e.g., less object details, small size and different viewpoints- see Fig. 3). On the other hand, as summarized in Table 1, most of the existing aerial image datasets are annotated with bounding boxes or point-labels that only coarsely localize the object instances.

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Figure 1: An example image from the COWC dataset 2. The Architecture. To detect cars in these large aerial images, we used the RetinaNet architecture.Published in 2017 by Facebook FAIR, this paper. Aerial Elephant, 2019-The Aerial Elephant Dataset: A New Public Benchmark for Aerial Object Detection. Vehicle Re-ID, [2019-Vehicle Re-identification in Aerial Imagery: Dataset and Approach] Okutama, 2017-Okutama-action:An aerial view video dataset for concurrent human action detection; Awesome Researchers Awesome Resources Projects. All datasets are compared on number of images, categories, annotations, image size, pixel numbers and color channels. If it is an aerial image dataset, we also provide the ground sample resolution (GSD). cls., det. and seg. stand for classification, detection and segmentation respectively Dataset Dataset 1: WHU Building Dataset . Summary: The dataset consists of an aerial image sub-dataset, two satellite image sub-datasets and a building change detection sub-dataset covering more than 1400 km 2. Paper: Fully Convolutional Networks for Multi-Source Building Extraction from An Open Aerial and Satellite Imagery Dataset The dataset consists of aerial imagery of Dubai obtained by MBRSC satellites and annotated with pixel-wise semantic segmentation in 6 classes. The total volume of the dataset is 72 images grouped into 6 larger tiles. The classes are: Building: #3C1098. Land (unpaved area): #8429F6

INRIA Aerial Image Labeling Dataset Papers With Cod

Dataset Download. The CVUSA dataset is available as five files form Google Drive. metadata.tar: A README and an example script. flickr_aerial.tar: aerial images associated with the Flickr images. streetview.tar: ground-level images from Google Street View. streetview_aerial.tar: aerial images associated with the Google Street View images Aerial Image Segmentation Dataset 80 high-resolution aerial images with spatial resolution ranging from 0.3 to 1.0. Images manually segmented. 80 Images Aerial Classification, object detection 2013 J. Yuan et al. KIT AIS Data Set Multiple labeled training and evaluation datasets of aerial images of crowds

This dataset contains 74 images of aerial maritime photographs taken with via a Mavic Air 2 drone and 1,151 bounding boxes, consisting of docks, boats, lifts, jetskis, and cars. This is a multi class problem. This is an aerial object detection dataset. This is a maritime object detection dataset. The drone was flown at 400 ft DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images. The images are collected from different sensors and platforms. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and contains objects exhibiting a wide variety of scales.

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Seven steps towards a satellite imagery dataset. Photo by NASA on Unsplash. S atellite images can be in the visible colors (RGB) and in other spectra, e.g. data within specific wavelength ranges across the electromagnetic spectrum like Near-Infrared. There are also elevation maps, usually made by radar images which can be used to estimate. This paper describes the images collected by a customized unmanned aerial vehicle (UAV) system from an open-pit gravel mine at approximate altitude of 80 m over an area of 150 m × 200 m ().These images are filtered and analyzed to produce additional data such as corresponding feature points (), which will allow implementing and evaluating any structure-from-motion or photogrammetric approach.

Aerial Imagery dataset for fire detection: classification

The Scanned Aerial Imagery raster type is designed for creating mosaic datasets from scanned aerial photos. The Frame Camera raster type can also add scanned aerial photos to a mosaic dataset; however, the Scanned Aerial Imagery raster type adds an image property that is used in the block adjustment process to choose the most appropriate. How to construct low-altitude aerial image datasets for deep learning. 1. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China. 2. Cyberspace Security Research Institute, China Electronics Technology Group Corporation, Xiong'an New Area 071000, China. 3 The EAGLE dataset consists of 8, 820 aerial images with size of 936 × 936 p x, acquired during several flight campaigns carried out between 2006 and 2019 in various time of day and year with different weather and illumination conditions.The images were taken under different traffic conditions and situations involving vehicles such as motorways, urban/rural areas, industrial districts, floods.

Sample of Mandan, North Dakota Aerial Image Dataset. Originally produced by the Farm Security Administration, these are georeferenced aerial images from Morton County, North Dakota. Historic print images housed at the Mandan, North Dakota ARS Long-Term Agricultural Research facility were digitized, georeferenced, and processed for use in both. The dataset used in this challenge is a subset of the Agriculture-Vision dataset [ 1 ]. The challenge dataset contains 21,061 aerial farmland images captured throughout 2019 across the US. Each image consists of four 512x512 color channels, which are RGB and Near Infra-red (NIR). Each image also has a boundary map and a mask Satellite Imagery Satellite imagery is described with access provided to image browsers, posters, historical imagery, and custom imagery. Satellite Datasets in Development NCEI continues to steward satellite data—checking dataset quality, producing climate records, and performing analyses. This area provides information to ongoing dataset.

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DOTA (Dataset for Object deTection in Aerial Images) DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images. The images are collected from different sensors and platforms. Each image is of the size in the range from 800 × 800 to 20,000 × 20,000 pixels and. Contest. You are very welcome to submit your results to the contest! The training set contains 180 color image tiles of size 5000×5000, covering a surface of 1500 m × 1500 m each (at a 30 cm resolution). There are 36 tiles for each of the following regions: The format is GeoTIFF (TIFF with georeferencing, but the images can be used as any. CITY-OSM: Learning Aerial Image Segmentation From Online Maps +20GB of aerial images obtained from Google Maps (including the groundtruth from OSM). Mut1ny Face/Headsegmentation dataset Head/face segmentation dataset contains over 16k labeled images. The Unsupervised LLAMAS dataset

An aerial imagery dataset for pile fire detection based on image classification and fire segmentation using deep learning: Type of data: Table Video Image Figure: How data were acquired: Full HD and 4K camera: Zenmuse X4S camera, Phantom 3 Camera Thermal Camera: FLIR Vue Pro R Drones: DJI Matrice 200, DJI Phantom 3 Professional: Data format. xView is one of the largest publicly available datasets of overhead imagery. It contains images from complex scenes around the world, annotated using bounding boxes. The DIUx xView 2018 Detection Challenge is focused on accelerating progress in four computer vision frontiers: 1 Reduce minimum resolution for detection

One of the challenges in developing aerial image datasets for agricultural pattern analysis is image size. Semantic segmentation for detecting field conditions over aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity, the researchers explain in the paper Agriculture-Vision: A Large. An aerial survey performed with a Falcon UAV fixed-wing drone over Red Rocks, Colorado. The sensor is a Canon Powershot SX260HS with GPS enabled. This example data set contains 45 high resolution oblique images for 3D model and point cloud creation. The original imagery and processed results are available for download Satellite Imagery Dataset for Machine Learning and AI. AI based models developed through machine learning for Aerial view need satellite imagery dataset to train the model for right detection. Anolytics provides satellite imagery data sets with annotated images to make the varied objects recognizable from the Aerial view or at sky level heights. 1998 Bartholomew County Aerial Photos; 2008 Allen County Orthophotography; 2011 Gibson & Posey Counties RGBI and Elevation; Historic Bloomington Aerial Photos; 1998 Bloomington Orthophotography; Elevation Datasets. 2002 Bradford Woods Elevation Contours and Orthophotography; 2002 Wayne County Contours Dataset WHU building dataset : 2-period aerial images containing 12,796 buildings, provided along with building vector and raster maps. SZTAKI Air change benchmark [5, 6] 13 aerial image pairs with 1.5 m spatial resolution, labeled as changed and unchanged at pixel level. OSCD.

A dataset of large-scale aerial images produced by Intelinair, a spinout from the University of Illinois at Urbana-Champaign, aims to give farmers visibility into the conditions of their fields. The dataset, called Agriculture-Vision, will enable agricultural pattern analysis of aerial images, providing farmers with actionable insights into the. Unmanned aerial image dataset: Ready for 3D reconstruction Data Brief. 2019 May 24;25:103962. doi: 10.1016/j.dib.2019.103962. eCollection 2019 Aug. Authors Mozhdeh Shahbazi 1 , Patrick Ménard 2 , Gunho Sohn 3 , Jérome Théau 4 Affiliations 1 Department of Geomatics. It can cover a much wider range of aerial images and better aproximate the real aerial image classification problem than existing dataset. In contrast with our AID, both the UC-Merced dataset [ 57 ] and WHU-RS19 dataset [ 7 ] contain 100 images per class and only 20 images in each class were usually used for testing the algorithms [ 57 , 23. The method integrates an aerial image dataset suitable for YOLO training by pro-cessing three public aerial image datasets. Experiments show that the training model has a good performance on unknown aerial images, especially for small objects, rotating objects, as well as compact and dense objects, while meeting the real-time requirements The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images.Dataset features. land cover from Poland, Central Europe 1; three spectral bands - RGB; 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px

Object Detection with Deep Learning on Aerial Imagery by

Eye image has five spectral bands that represent blue, green, red, red edge, and near infrared spectral values. We get valid images at fifteen time stamps over two scenes, with at least one image per month. Scene 1 is a simpler dataset with six crop types, while Scene 2 is a more complicated, sporadic dataset with nine crop types, see Figure 1 The unmanned aerial vehicles (UAVs) significantly contribute to the convenience and intelligence of life. However, the large use of UAVs also leads to high security risk. Only detecting the small and flying UAVs can prevent the safety accidents. UAV detection task could be regarded as a branch of object detection in flied of image processing. The advanced object detection models are mainly. Planet, a satellite imaging company, recently released a dataset of more than 100,000 images from the Amazon basin and sponsored a Kaggle competition involving label-ing the atmosphere and ground features in the images [1]. Each image is 256 x 256 pixels and has RGB and near-infrared channels. Notably, these images have at least te Welcome to the State Geographic Information Database (SGID) The SGID aims to be the primary source for state-wide GIS data in Utah by creating a single portal to datasets created by many different agencies and organizations. With such a broad scope, SGID datasets live in many different places depending on their type, availability, and popularity This dataset will be used for training the deep neural network, to 'teach' it to recognize roads given a satellite image. There already is an available dataset for this specific problem named Massachusetts Roads Dataset. An example of the pictures and road topology representations contained in the dataset is

2.1 Datasets Based on TTs Images Only The dataset, introduced in [23], is employed to detect TTs from the aerial images. The dataset consists of 3,200 images in which only 1,600 images contain towers while the rest contains background only. The images size is 64⇥128 pixels which is scaled down from the original frame sizes (550⇥480 and 720. Field Value; Source: https://opendata-cosagis.opendata.arcgis.com/datasets/e866cfe4366e492c8f5654c2faa09392: Last Updated: June 27, 2021, 02:00 (UTC) Create


HxIP is a high-quality, accurate and professional imagery dataset. The imagery within the program is 4-band (RGBN) ortho with 30cm (1 foot) resolution for the Wide Area Coverage and within the United States, 15cm (6 inch) for the Urban Area Coverage (defined as >50,000 population). The program also encompasses set specification and accuracy. What would be a good aerial imagery dataset ? Would it be possible to have access to kespry aerial imagery dataset ? It's featured in many blogs and example from Nvidia, but I can't find it anywhere to use it train a model for classification or detection task. Thank you

Explore the World in Real-Time Launch web map in new window NOAA Satellite Maps - Latest 3D Scene This high-resolution imagery is provided by geostationary weather satellites permanently stationed more than 22,000 miles above the Earth. Use this web map to zoom in on real-time weather patterns developing around the world. Download imagery via the maps below This dataset provides maritime scenes of optical aerial images from visible spectrum. The MASATI dataset contains color images in dynamic marine environments, and it can be used to evaluate ship detection methods. Each image may contain one or multiple targets in different weather and illumination conditions

GID Dataset - GitHub Page

SLAR images are useful to scientists studying geologic structures. SLAR images most often consist of image strips and 1:250,000-scale mosaics prepared from these strips. Some SLAR data are available on CD-ROM. The images below show the Pentagon and vicinity, Washington D.C. , portions of the National Aerial Photograph Program at 9, 18, and 36-inch The Datasets tab answers the question: What satellite or aerial imagery are you looking for? The USGS Earth Explorer remote sensing datasets are plentiful: aerial imagery, AVHRR, commercial imagery, digital elevation models, Landsat, LiDAR, MODIS, Radar and more. It depends on the date and time for which Landsat scene you can download

Planet Archive empowers customers with a living dataset of global change, with new imagery added on a daily basis. With 10+ billion sq km of imagery, Planet Archive has proprietary datasets back to 2009 and public datasets back to 1972. Get the most recent before and after image available. Monitor historical change and assess trends globally Satellite imagery is now complemented by aerial photos of individual countries. Now you can see complete USA, Netherlands, Denmark and selected cities like Prague, Zurich, and others. All data is available as one satellite layer in MapTiler Cloud. We are also providing data for self-hosting Dataset description: The datasets are encoded as MATLAB .mat files that can be read using the standard load command in MATLAB. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are 1x4 and 1x6 vectors for SAT-4 and SAT-6 respectively having a single 1 indexing a. These eight aerial photographs show land-use and urban density changes between 1938 and 2006 at four locations in Salt Lake and Utah Counties. The 1938 photos were taken as part of the U.S. Bureau of Reclamation Salt Lake Aqueduct Project. The 2006 photos were taken as part of the U.S. Department of Agriculture National Agriculture Imagery Program Naira Hovakimyan A dataset of large-scale aerial images produced by Intelinair, a spinout from the University of Illinois at Urbana-Champaign, aims to give farmers visibility into the conditions of their fields. The dataset, called Agriculture-Vision, will enable agricultural pattern analysis of aerial images, providing farmers with actionable insights into the performance of their crops to.

GOHSEP 2010 Aerial Imagery. This dataset contains 6-inch statewide Digital Orthophoto Quarter-Quarter Quadrangle (DOQQQ) imagery provided by the Louisiana Governor's Office of Homeland Security and Emergency Preparedness (GOHSEP) ICEYE IMAGERY ARCHIVE - 18,000 SAR SATELLITE IMAGE THUMBNAILS. Access the public archive of radar imagery previews acquired with the ICEYE SAR satellite constellation. Download Archive. The dataset includes 2 Spot images of Kuala Lumpur International Airport, Malaysia & Suvarnabhumi Airport, Bangkok, Thailand. Download With aerial photography and satellite imagery, sometimes the location information delivered with them is inadequate, and the data does not align properly with other data you have. Thus, to use some raster datasets in conjunction with your other spatial data, you may need to align or georeference them to a map coordinate system

Lakeland Florida Us City Street Map Stock IllustrationFull-Resolution Residual Networks (FRRNs) for Semantic

DLR-ACD is the first dataset of its kind, the researchers write, and they hope to use it to promote research on aerial crowd analysis. The majority of the images in ACD contain many thousands of people viewed from overhead, whereas most other aerial datasets involves crowds of less than 1,000 in size, according to analysis by the researchers These datasets are perfect for long-term projects and projects requiring local data for analysis. Because the data is stitched together in a mosaic, the result is a seamless dataset ready for analysis. Data sets include the most recent 30cm Orthophoto mosaics, stereo imagery, and 30cm DSMs. Default projection for EU coverage is WGS84 Citation If you find this dataset useful, please cite this paper (and refer the data as Stanford Drone Dataset or SDD): A. Robicquet, A. Sadeghian, A. Alahi, S. Savarese, Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes in European Conference on Computer Vision (ECCV), 2016 The dataset contains time-synchronized high-resolution images (1920 x 1080 x 24 bits), GPS, IMU, and ground level Google-Street-View images. The high-resolution aerial images were captured with a rolling shutter GoPro Hero 4 camera that records each image frame line by line, from top to bottom with a readout time of 30 millisecond