kaggle stanford drone dataset

2022.07.31
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kaggle stanford drone dataset

slightly different versions of the same dataset. : (Common Objects in Context) is a large-scale dataset object detection, segmentation, and captioning dataset. It should be noted that the dataset was gathered utilising a variety of drone platforms (i.e., drones of various types), in a variety of settings, and under a variety of weather and lighting circumstances. 2018, Open AI Challenge: Aerial Imagery of South Pacific Islands (WeRobotics & Worldbank, May 2018) Three challenge tracks: Road Extraction, Building Detection, Land cover classification, Paper: Demir et al. for 5.7 km2 of Munich, Germany. 2019. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 2020, SEN12MS-CR-TS - Ebel et al. Visdrone-DET Dataset Citation Information. A ChemImage Company - 2022 Innotescus, LLC. Includes clear, cloud and cloud-shadow classes. 2019. Please see these fantastic ressources for more recent datasets: 15 categories from plane to bridge, 188k instances, object instances and segmentation masks (MS COCO format), Google Earth & JL-1 image chips, Faster-RCNN baseline model (MXNet), devkit, Academic use only, replaces DOTA dataset, Paper: Zamir et al. - All rights reserved. Paper: Azimi et al. Garnot & Landrieu 2021. xView3 Dark Vessel Detection 2021 (xView3 Team, Aug 2021) Synthetic (630k planes, 50k images) and real (14.7k planes, 253 Worldview-3 images (0.3m res. Thank you for your contribution to the ML community! 2018, SpaceNet 3: Road Network Detection (CosmiQ Works, Radiant Solutions, Feb 2018) 2343 image chips (drone imagery), 10 landcover categories (background, water, building flooded, building non-flooded, Building footprints (Rio de Janeiro), 3/8band Worldview-3 imagery (0.5m res. Our favorite source for free datasets, collaboration, and competition is Kaggle. Paper: Slovenia Land Cover Classification (Sinergise, Feb 2019) 2 main categories corn and soybeans, Landsat 8 imagery (30m res. Highly accurate street lane markings (12 categories e.g. SpaceNet: Multi-Sensor All-Weather Mapping (CosmiQ Works, Capella Space, Maxar, AWS, Intel, Feb 2020) 2019. ), Paper: Hughes, J.M.

all, Jan 2020) 2017, EuroSAT (DFK, Aug 2017) 2019, Statoil/C-CORE Iceberg Classifier Challenge (Statoil/C-CORE, Jan 2018) ), 5 cities, SpaceNet Challenge Asset Library, SpaceNet 1: Building Detection v1 (CosmiQ Works, Radiant Solutions, NVIDIA, Jan 2017) over 2 years, 75 aois, landcover labels (7 categories), 2 competition tracks (Binary land cover classification & multi-class change detection). A multi-modal and mono-temporal data set for cloud removal. Visdrone-DET validation split comprises 1580 images. Since 2018 Microsoft research open data has been collaborating across the research community to collect datasets for a variety of categories. 2014, Biome: L8 Cloud Cover Validation data (USGS, 2016) 20 land cover categories by fusing three data sources: Multispectral LiDAR, Hyperspectral (1m), RGB imagery (0.05m res.

Building footprints & 3 building conditions, RGB UAV imagery - Link to data, LPIS agricultural field boundaries Denmark - Netherlands - France FloodNet (University of Maryland, Jun 2021) 2018, Urban 3D Challenge (USSOCOM, Dec 2017) ), 51 GB, Cactus Aerial Photos (CONACYT Mexico, Jun 2018) 2017, Inria Aerial Image Labeling (inria.fr) 13 land cover categories + 4 cloud condition categories, 4-band (RGB-NIR) satelitte imagery (5m res. Buildings footprints, RGB satellite imagery, COCO data format, SpaceNet 2: Building Detection v2 (CosmiQ Works, Radiant Solutions, NVIDIA, May 2017) 2020, iSAID: Large-scale Dataset for Object Detection in Aerial Images (IIAI & Wuhan University, Dec 2019) Predict building roof type (5 categories, e.g. So2Sat LCZ42 (TUM Munich & DLR, Aug 2018) Corresponding imagery from drone, satellite and ground camera of 1,652 university buildings, Paper: Zheng et al. 2020. Paper: Xia et al. 2019, xView 2 Building Damage Asessment Challenge (DIUx, Nov 2019) . ), Reunion island.

12.6mil (Canada) & 125.2mil (USA) & 17.9mil (Uganda/Tanzania) & 11.3mil (Australia) building footprints, GeoJSON format, delineation based on Bing imagery using ResNet34 architecture. 34701 manually segmented 384x384 patches with cloud masks, Landsat 8 imagery (R,G,B,NIR; 30 m res. Some tasks are inferred based on the benchmarks list. Airbus Aircraft Detection (Airbus, Mar 2021) 21 land cover categories from agricultural to parkinglot, 100 chips per class, aerial imagery (0.30m res. 6 urban land cover classes, raster mask labels, 4-band RGB-IR aerial imagery (0.05m res.) ), SpaceNet Challenge Asset Library, Paper: Van Etten et al. Hub users may have access to a variety of publicly available datasets. The 8-12-value protocol is consistent with the most trajectory forecasting approaches, usually focused on the 5-dataset ETH-univ + ETH-hotel + UCY-zara01 + UCY-zara02 + UCY-univ. 60 aerial UAV videos over Stanford campus and bounding boxes, 6 classes (Pedestrian, Biker, Skateboarder, Cart, Car, Bus), Paper: Robicquet et al. 10 land cover categories from crops to vehicle small, 57 1x1km images, 3/16-band Worldview 3 imagery (0.3m-7.5m res. Agriculture-Vision Database & CVPR 2020 challenge (UIUC, Tree position & 4 tree species, RGB UAV imagery (0.4m/0.8m res. Paper: Castillo-Navarro et al., 2021, LandCoverNet: A Global Land Cover Classification Training Dataset (Alemohammad S.H., et al., Jul 2020) buildings, roads, vegetation). Tools. 32k car bounding boxes, aerial imagery (0.15m res. Tree position, tree species and crown parameters, hyperspectral (1m res.) 10 land cover categories from industrial to permanent crop, 27k 64x64 pixel chips, 3/16 band Sentinel-2 satellite imagery (10m res. 157k building footprint masks, RGB orthophotos (0.5m res. satellite imagery, LiDAR (0.80m pulse spacing, ASCII format), semantic labels, urban setting USA, baseline methods provided, Paper: Le Saux et al. The Street View House Numbers (SVHN) Dataset. Paper: Visdrone-DET training split comprises 6471 images. scattered trees), 400k 32x32 pixel chips covering 42 cities (LCZ42 dataset), Sentinel 1 & Sentinel 2 (both 10m res. Maritime object bounding boxes for 1k Sentinel-1 scenes (VH & VV polarizations), ancillary data (land/ice mask, bathymetry, wind speed, direction, quality).

We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. 10 land cover classes, temporal stack of hyperspectral Sentinel-2 imagery (R,G,B,NIR,SWIR1,SWIR2; 10 m res.) Airbus Ship Detection Challenge (Airbus, Nov 2018) 126k building footprints (Atlanta), 27 WorldView 2 images (0.3m res.) extracted from the 2009 National Agriculture Imagery Program (NAIP), Paper: Basu et al. WiDS Datathon 2019 : Detection of Oil Palm Plantations (Global WiDS Team & West Big Data Innovation Hub, Jan 2019) 17k aerial photos, 13k cactus, 4k non-actus, Kaggle kernels, Paper: Lpez-Jimnez et al. Land cover classification based on SEN12MS dataset (see category Semantic Segmentation on this list), low- and high-resolution tracks. ), Paper: Xu et al. & DSM, 38 image patches. Weekly Planetscope time-series (3m res.) ), covering cities in 30 countries, Paper: Helber et al. ), 6 cities, Paper: Mundhenk et al. ), Paper: Mohajerani et al. Curious about applying augmentation to computer vision datasets? Please feel free to, Talk Title:"Microengineered tissues for regenerative medicine and organs-on-a-chip applications", IEEE CAS Charles Desoer Life Science Systems Student Attendance Grant, Assistive, Rehabilitation, and Quality of Life Technologies, Bio-inspired and Neuromorphic Circuits and Systems, Biofeedback, Electrical Stimulation, and Closed-Loop Systems, Biomedical Imaging Technologies & Image Processing, Innovative Circuits for Medical Applications, Medical Information Systems and Bioinformatics, Wireless and Energy Harvesting/Scavenging Technology. SpaceNet 4: Off-Nadir Buildings (CosmiQ Works, DigitalGlobe, Radiant Solutions, AWS, Dec 2018) Paper: Chiu et al. Paper: Shermeyer et al. 2300 image chips, street geometries with location, shape and estimated travel time, 3/8band Worldview-3 imagery (0.3m res. Building footprint masks, RGB aerial imagery (0.3m res. 2 categories ship and iceberg, 2-band HH/HV polarization SAR imagery, Kaggle kernels, Functional Map of the World Challenge (IARPA, Dec 2017) 2343 UAV images from after Hurricane Harvey, landcover labels (10 categories, e.g.

Garnot & Landrieu 2021. DroneDeploy Segmentation Dataset (DroneDeploy, Dec 2019) 2017, Planet: Understanding the Amazon from Space (Planet, Jul 2017) 6 land cover categories, 400k 28x28 pixel chips, 4-band RGBNIR aerial imagery (1m res.) ), Amazonian rainforest, Kaggle kernels, AID: Aerial Scene Classification (Xia et al., 2017) We are excited to hear from the following at the BioCAS 2015 Gala Dinner Forum, "The most important problems to be tackled by the BioCAS community": Join the following at the BioCAS 2015 Parallel Workshop, "Lessons Learned Along the Translational Highway": Steve Maschino,Cyberonics, Inc., Intermedics, Jared William Hansen, North Dakota State University, Johanna Neuber, University of Texas at Austin, Muhammad Awais Bin Altaf, Masdar Institute of Science and Technology, Piyakamal Dissanayaka Manamperi, RMIT University, Mami Sakata, Yokohama National University, Elham Shabani Varaki, University of Western Sydney, Mahdi Rasouli, National University of Singapore, A Smart Homecage System with Behavior Analysis and Closed-Loop Optogenetic Stimulation Capacibilities, Yaoyao Jia, Zheyuan Wang, Abdollah Mirbozorgi, Maysam GhovanlooGeorgia Institute of Technology, A 12-Channel Bidirectional Neural Interface Chip with Integrated Channel-Level Feature Extraction and PID Controller for Closed-Loop Operation, Xilin Liu, Milin Zhang, Andrew Richardson, Timothy Lucas, Jan Van der SpiegelUniversity of Pennsylvania, A Wireless Optogenetic Headstage with Multichannel Neural Signal Compression, Gabriel Gagnon-Turcotte, Yoan Lechasseur, (Doric Lenses Inc.), Cyril Bories, Yves De Koninck, Benoit GosselinUniversit Laval, 32k Channels Readout IC for Single Photon Counting Detectors with 75 m Pitch, ENC of 123 e- rms, 9 e- rms Offset Spread and 2% rms Gain Spread, Pawel Grybos, Piotr Kmon, Piotr Maj, Robert SzczygielAGH University of Science and Technology, BioCAS 2015 - Atlanta, Georgia, USA - October 22-24, 2015. 1980 image chips of 256 x 256 pixels in V1.0 spanning 66 tiles of Sentinel-2. ), COCO data format, baseline models, Paper: Christie et al. 5 sea lion categories, ~ 80k instances, ~ 1k aerial images, Kaggle kernels, Stanford Drone Data (Stanford University, Oct 2016) Intelinair, CVPR, Jan 2020) author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin}. dash line, long line, zebra zone) & urban infrastructure (19 categories e.g. ), DSM/DTM, 3 cities, SpaceNet Challenge Asset Library, DSTL Satellite Imagery Feature Detection Challenge (Dstl, Feb 2017) The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3.2 seconds) i.e., t7,t6,,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4.8 seconds), i.e., t+1,,t+12. 4 cloud categories (cloud, thin cloud, cloud shadows, clear), 96 Landsat 8 scenes (30m res. 131k ships, 104k train / 88k test image chips, satellite imagery (1.5m res. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other). : A great source of data for a wide range of tasks in autonomous driving. Instead of downloading the Visdrone-DET dataset in Python, you can effortlessly load it in Python via our. University-1652: Drone-based Geolocalization (Image Retrieval) (ACM Multimedia, Oct 2020) ), multiple AOIs in Tonga, NIST DSE Plant Identification with NEON Remote Sensing Data (inria.fr, Oct 2017) Netherlands: 294 crop/vegetation catgeories, 780k parcels, CrowdAI Mapping Challenge (Humanity & Inclusion NGO, May 2018) for year 2017 with cloud masks, Official Slovenian land use land cover layer as ground truth. 2019 Outcome Part A: Kunwar et al. Predict the chronological order of images taken at the same locations over 5 days, Kaggle kernels. ), 5 cities, ISPRS Potsdam 2D Semantic Labeling Contest (ISPRS) Testing is requested on diverse partitions of BIWI Hotel, Crowds UCY, Stanford Drone Dataset, and is evaluated by a specific server (ground-truth testing data is unavailable for applicants). Citation: Alemohammad S.H., et al., 2020 and blog post, LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery (Boguszewski, A., et al., May 2020)

journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}. 2018. satellite-image-deepl-learning & ), manual segmentations masks for Buildings, Woodland and Water, Paper: Boguszewski et al., 2020, 95-Cloud: A Cloud Segmentation Dataset (S. Mohajerani et. 2016, LoveDA (Wuhan University, Oct 2021)

), pre-trained baseline model. 5987 image chips (Google Earth), 7 landcover categories, 166768 labels, 3 cities in China. Detection of settlements without electricity, 98 multi-temporal/multi-sensor tiles ( Sentinel-1, Sentinel-2, Landsat-8, VIIRS), per chip & per pixel labels (contains buildings, presence electricity). 180,748 corresponding image triplets containing Sentinel-1 (VV&VH), Sentinel-2 (all bands, cloud-free), and MODIS-derived land cover maps (IGBP, LCCS, 17 classes, 500m res.). Bi-cubicly resampled to same number of pixels in each image to counter courser native resolution with higher off-nadir angles, Paper: Weir et al. boxes: tensor representing bounding box for the object of interest. are present. ), SpaceNet Challenge Asset Library. 10000 aerial images within 30 categories (airport, bare land, baseball field, beach, bridge, ) collected from Google Earth imagery. road-flooded, ). res) timeseries for 2 years, 100 locations around the globe, for building footprint evolution & address propagation. Train a model on Visdrone-DET dataset with PyTorch in Python, dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False), Train a model on Visdrone-DET dataset with TensorFlow in Python, https://github.com/VisDrone/VisDrone-Dataset, Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin: Detection and Tracking Meet Drones Challenge, Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin, Visdrone-DET Dataset Licensing Information.