AFO - Aerial dataset of floating objects (Gasienica-Jzkowy et al, Jun 2020) Here is an immediate link to the Github repository if you want to just dive straight into the code. 2017, Deepsat: SAT-4/SAT-6 airborne datasets (Louisiana State University, 2015) ), covering cities in 30 countries, Paper: Helber et al. Be the first one to, github.com-robmarkcole-satellite-image-deep-learning_-_2022-10-18_20-24-04, Advanced embedding details, examples, and help, satellite-image-deep-learning group on LinkedIn, Object detection with rotated bounding boxes, Object detection enhanced by super resolution, Autoencoders, dimensionality reduction, image embeddings & similarity search, Image Captioning & Visual Question Answering, Self-supervised, unsupervised & contrastive learning, Terrain mapping, Disparity Estimation, Lidar, DEMs & NeRF, Deep learning in remote sensing applications: A meta-analysis and review, A brief introduction to satellite image classification with neural networks, Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras, Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier, Land-Cover-Classification-using-Sentinel-2-Dataset, Land Cover Classification of Satellite Imagery using Convolutional Neural Networks, Detecting deforestation from satellite images, Neural Network for Satellite Data Classification Using Tensorflow in Python, Slums mapping from pretrained CNN network, Comparing urban environments using satellite imagery and convolutional neural networks, Land Use and Land Cover Classification using a ResNet Deep Learning Architecture, Vision Transformers Use Case: Satellite Image Classification without CNNs, Scaling AI to map every school on the planet, Understanding the Amazon Rainforest with Multi-Label Classification + VGG-19, Inceptionv3, AlexNet & Transfer Learning, Implementation of the 3D-CNN model for land cover classification, Land cover classification of Sundarbans satellite imagery using K-Nearest Neighbor(K-NNC), Support Vector Machine (SVM), and Gradient Boosting classification algorithms, Satellite image classification using multiple machine learning algorithms, wildfire-detection-from-satellite-images-ml, Classifying Geo-Referenced Photos and Satellite Images for Supporting Terrain Classification, Remote-Sensing-Image-Classification-via-Improved-Cross-Entropy-Loss-and-Transfer-Learning-Strategy, A brief introduction to satellite image segmentation with neural networks, Satellite Image Segmentation: a Workflow with U-Net, How to create a DataBlock for Multispectral Satellite Image Semantic Segmentation using Fastai, Using a U-Net for image segmentation, blending predicted patches smoothly is a must to please the human eye, Satellite-Image-Segmentation-with-Smooth-Blending, Semantic Segmentation of Satellite Imagery using U-Net & fast.ai, HRCNet-High-Resolution-Context-Extraction-Network, Semantic segmentation of SAR images using a self supervised technique, Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels, Remote-sensing-image-semantic-segmentation-tf2, Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation, U-Net for Semantic Segmentation on Unbalanced Aerial Imagery, Semantic Segmentation of Dubai dataset Using a TensorFlow U-Net Model, Automatic Detection of Landfill Using Deep Learning, Multi-class semantic segmentation of satellite images using U-Net, Codebase for multi class land cover classification with U-Net, Satellite Imagery Semantic Segmentation with CNN, Aerial Semantic Segmentation using U-Net Deep Learning Model, DeepGlobe Land Cover Classification Challenge solution, Semantic-segmentation-with-PyTorch-Satellite-Imagery, Semantic Segmentation With Sentinel-2 Imagery, Large-scale-Automatic-Identification-of-Urban-Vacant-Land, r field boundary detection: approaches and main challenges, Whats growing there? 8 classes (inc. cloud and cloud shadow) for 38 Sentinel-2 scenes (10 m res.). I sometimes write too. Model accuracy falls off rapidly as image resolution degrades, so it is common for object detection to use very high resolution imagery, e.g. 2020, iSAID: Large-scale Dataset for Object Detection in Aerial Images (IIAI & Wuhan University, Dec 2019) It wasnt anything really special; we began with 5 layers and noticed that performance improved slightly as we added more layers. Then we create dataset and data loader for training and validation with the preferred batch_size, feel free to experiment with more transformation which might help you to improve accuracy. * This article compares a number of ML algorithms, random forests, stochastic gradient descent, support vector machines, Bayesian method. 2019, Statoil/C-CORE Iceberg Classifier Challenge (Statoil/C-CORE, Jan 2018) SRCDNet is designed to learn and predict change maps from bi-temporal images with different resolutions* Model-Guided Deep Hyperspectral Image Super-resolution -> code accompanying the paper Model-Guided Deep Hyperspectral Image Super-Resolution* Super-resolving beyond satellite hardware -> paper assessing SR performance in reconstructing realistically degraded satellite images* satellite-pixel-synthesis-pytorch -> PyTorch implementation of NeurIPS 2021 paper: Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis* SRE-HAN -> Squeeze-and-Residual-Excitation Holistic Attention Network improves super-resolution (SR) on remote-sensing imagery compared to other state-of-the-art attention-based SR models* satsr -> A project to perform super-resolution on multispectral images from any satellite, including Sentinel 2, Landsat 8, VIIRS &MODIS* OLI2MSI -> dataset for remote sensing imagery super-resolution composed of Landsat8-OLI and Sentinel2-MSI images* MMSR -> Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution* HSRnet -> code for the 2021 paper: Hyperspectral Image Super-resolution via Deep Spatio-spectral Attention Convolutional Neural Networks* RRSGAN -> code for 2021 paper: RRSGAN: Reference-Based Super-Resolution for Remote Sensing Image* HDR-DSP-SR -> code for 2021 paper: Self-supervised multi-image super-resolution for push-frame satellite images, Note that nearly all the MISR publications resulted from the PROBA-V Super Resolution competition* deepsum -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)* 3DWDSRNet -> code to reproduce Satellite Image Multi-Frame Super Resolution (MISR) Using 3D Wide-Activation Neural Networks* RAMS -> Official TensorFlow code for paper Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks* TR-MISR -> Transformer-based MISR framework for the the PROBA-V super-resolution challenge. DroneDeploy Segmentation Dataset (DroneDeploy, Dec 2019) ), 51 GB, Cactus Aerial Photos (CONACYT Mexico, Jun 2018) Note that since FN is not possible in object detection, ROC curves are not appropriate. Land cover time series classification (9 categories), Landsat-8 (23 images time series, 10 band features, 30m res. 10 land cover categories from industrial to permanent crop, 27k 64x64 pixel chips, 3/16 band Sentinel-2 satellite imagery (10m res. * How not to test your deep learning algorithm? A convolutional neural network model used to detect hurricane damage in RGB satellite images* wildfireforecasting -> code for 2021 paper: Deep Learning Methods for Daily Wildfire Danger Forecasting. Since the Sentinel-2 satellite constellation will scan the Earths land surface for about the next 2030 years on a repeat cycle of about five days, a trained classifier can be used for monitoring land surfaces and detect changes in land use or land cover. The availability of free cloud computing with enough computational power (like google colab, kaggle) to train large ML models also act as a stepping stone for this trend. Blog post MEET THE WINNERS OF THE OVERHEAD GEOPOSE CHALLENGE* cars -> a dedicated and open source 3D tool to produce Digital Surface Models from satellite imaging by photogrammetry. The main types of changes in the dataset include: (a) newly built urban buildings; (b) suburban dilation; (c) groundwork before construction; (d) change of vegetation; (e) road expansion; (f) sea construction. A weakly-supervised approach, training with only image-level labels* weak-segmentation -> Weakly supervised semantic segmentation for aerial images in pytorch* TNNLS2022X-GPN -> Code for paper: Semisupervised Cross-scale Graph Prototypical Network for Hyperspectral Image Classification* weakly_supervised -> code for the paper Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. from Copernicus UrbanAtlas 2012), designed for semi-supervised semantic segmentation. Sentinel-1 & Sentinel-2, 2018. The vast majority of the other image pairs we had were not so clear cut. Identify crops from multi-spectral remote sensing data (Sentinel 2), Tree species classification from from airborne LiDAR and hyperspectral data using 3D convolutional neural networks, Find sports fields using Mask R-CNN and overlay on open-street-map, Detecting Agricultural Croplands from Sentinel-2 Satellite Imagery, Segment Canopy Cover and Soil using NDVI and Rasterio, Use KMeans clustering to segment satellite imagery by land cover/land use, U-Net for Semantic Segmentation of Soyabean Crop Fields with SAR images, Crop identification using satellite imagery, Flood Detection and Analysis using UNET with Resnet-34 as the back bone, Automatic Flood Detection from Satellite Images Using Deep Learning, UNSOAT used fastai to train a Unet to perform semantic segmentation on satellite imageries to detect water, Semi-Supervised Classification and Segmentation on High Resolution Aerial Images - Solving the FloodNet problem, A comprehensive guide to getting started with the ETCI Flood Detection competition, Map Floodwater of SAR Imagery with SageMaker, 1st place solution for STAC Overflow: Map Floodwater from Radar Imagery hosted by Microsoft AI for Earth, Flood Event Detection Utilizing Satellite Images, River-Network-Extraction-from-Satellite-Image-using-UNet-and-Tensorflow, semantic segmentation model to identify newly developed or flooded land, SatelliteVu-AWS-Disaster-Response-Hackathon, A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS, Landslide-mapping-on-SAR-data-by-Attention-U-Net, Methane-detection-from-hyperspectral-imagery, Road detection using semantic segmentation and albumentations for data augmention, Semantic segmentation of roads and highways using Sentinel-2 imagery (10m) super-resolved using the SENX4 model up to x4 the initial spatial resolution (2.5m), Winning Solutions from SpaceNet Road Detection and Routing Challenge, Detecting road and road types jupyter notebook, RoadTracer: Automatic Extraction of Road Networks from Aerial Images, Road-Network-Extraction using classical Image processing, Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques, Road and Building Semantic Segmentation in Satellite Imagery, find-unauthorized-constructions-using-aerial-photography, Semantic Segmentation on Aerial Images using fastai, Building footprint detection with fastai on the challenging SpaceNet7 dataset, Pix2Pix-for-Semantic-Segmentation-of-Satellite-Images, JointNet-A-Common-Neural-Network-for-Road-and-Building-Extraction, Mapping Africas Buildings with Satellite Imagery: Google AI blog post, How to extract building footprints from satellite images using deep learning, Semantic-segmentation repo by fuweifu-vtoo, Extracting buildings and roads from AWS Open Data using Amazon SageMaker, Remote-sensing-building-extraction-to-3D-model-using-Paddle-and-Grasshopper, Mask RCNN for Spacenet Off Nadir Building Detection, UNET-Image-Segmentation-Satellite-Picture, Vector-Map-Generation-from-Aerial-Imagery-using-Deep-Learning-GeoSpatial-UNET, Boundary Enhancement Semantic Segmentation for Building Extraction, Fusing multiple segmentation models based on different datasets into a single edge-deployable model, Visualizations and in-depth analysis .. of the factors that can explain the adoption of solar energy in .. Virginia, DeepSolar tracker: towards unsupervised assessment with open-source data of the accuracy of deep learning-based distributed PV mapping, Predicting the Solar Potential of Rooftops using Image Segmentation and Structured Data, Instance segmentation of center pivot irrigation system in Brazil, Oil tank instance segmentation with Mask R-CNN, Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN, Object-Detection-on-Satellite-Images-using-Mask-R-CNN, Things and stuff or how remote sensing could benefit from panoptic segmentation, Panoptic Segmentation Meets Remote Sensing (paper), Object detection on Satellite Imagery using RetinaNet, Tackling the Small Object Problem in Object Detection, Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review, awesome-aerial-object-detection bu murari023, Object Detection Accuracy as a Function of Image Resolution, Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN), Announcing YOLTv4: Improved Satellite Imagery Object Detection, Tensorflow Benchmarks for Object Detection in Aerial Images, Pytorch Benchmarks for Object Detection in Aerial Images, Faster RCNN for xView satellite data challenge, How to detect small objects in (very) large images, Object Detection Satellite Imagery Multi-vehicles Dataset (SIMD), Synthesizing Robustness YOLTv4 Results Part 2: Dataset Size Requirements and Geographic Insights, Object Detection On Aerial Imagery Using RetinaNet, Clustered-Object-Detection-in-Aerial-Image, Object-Detection-YoloV3-RetinaNet-FasterRCNN, HIECTOR: Hierarchical object detector at scale, Detection of Multiclass Objects in Optical Remote Sensing Images, Panchromatic to Multispectral: Object Detection Performance as a Function of Imaging Bands, Interactive-Multi-Class-Tiny-Object-Detection, Mid-Low Resolution Remote Sensing Ship Detection Using Super-Resolved Feature Representation, Reading list for deep learning based Salient Object Detection in Optical Remote Sensing Images, Machine Learning For Rooftop Detection and Solar Panel Installment, Follow up article using semantic segmentation, Building Extraction with YOLT2 and SpaceNet Data, Detecting solar panels from satellite imagery, Automatic Damage Annotation on Post-Hurricane Satellite Imagery. Lowering the classification threshold returns more true positives, but also more false positives. 2020, IEEE Data Fusion Contest 2022 (IEEE GRSS, Universit Bretagne-Sud, ONERA, ESA, Jan 2022) 10000 aerial images within 30 categories (airport, bare land, baseball field, beach, bridge, ) collected from Google Earth imagery. Uses LEVIR-CD & WHU-CD datasets* FHD -> code for 2022 paper: Feature Hierarchical Differentiation for Remote Sensing Image Change Detection* Change detection with Raster Vision -> blog post with Colab notebook* building-expansion -> code for 2021 paper: Enhancing Environmental Enforcement with Near Real-Time Monitoring: Likelihood-Based Detection of Structural Expansion of Intensive Livestock Farms* SaDLCD -> code for 2022 paper: Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection* EGCTNet_pytorch -> code for 2022 paper: Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer* S2-cGAN -> code for 2020 paper: S2-cGAN: Self-Supervised Adversarial Representation Learning for Binary Change Detection in Multispectral Images, More general than change detection, time series observations can be used for applications including improving the accuracy of crop classification, or predicting future patterns & events. Used Detectron2 and synthetic data with Unreal, superior performance to using Mask RCNN* Locate buildings with a dark roof that feed heat island phenomenon using Mask RCNN -> with repo, used INRIA dataset & labelme for annotation* CircleFinder -> Circular Shapes Detection in Satellite Imagery, 2nd place solution to the Circle Finder Challenge* LawnmaskRCNN -> Detecting lawns from satellite images of properties in the Cedar Rapids area using Mask-R-CNN* CropMaskRCNN -> Segmenting center pivot agriculture to monitor crop water use in drylands with Mask R-CNN and Landsat satellite imagery* Mask RCNN for Spacenet Off Nadir Building Detection* CATNet -> code for 2021 paper: Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images* Object-Detection-on-Satellite-Images-using-Mask-R-CNN -> detect ships* FactSeg -> Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS), also see FarSeg and FreeNet, implementations of research paper* aquapython -> detecting aquaculture farms using Mask R-CNN, Several different techniques can be used to count the number of objects in an image. for a small object class which may be under represented in your training dataset, use image augmentation* In general, larger models will outperform smaller models, particularly on challenging tasks such as detecting small objetcs* If model performance in unsatisfactory, try to increase your dataset size before switching to another model architecture* In training, whenever possible increase the batch size, as small batch sizes produce poor normalization statistics* The vast majority of the literature uses supervised learning with the requirement for large volumes of annotated data, which is a bottleneck to development and deployment. A recurrent neural network approach to encode multi-temporal data for land cover classification* timematch -> code for 2022 paper: A method to perform unsupervised cross-region adaptation of crop classifiers trained with satellite image time series. Note that GeoJSON is widely used by remote sensing researchers but this annotation format is not commonly supported in general computer vision frameworks, and in practice you may have to convert the annotation format to use the data with your chosen framework. With paper* Satellite Imagery Road Segmentation -> intro articule on Medium using the kaggle Massachusetts Roads Dataset* Label-Pixels -> for semantic segmentation of roads and other features* Satellite-image-road-extraction -> code for 2018 paper: Road Extraction by Deep Residual U-Net* roadbuildingextraction -> Pytorch implementation of U-Net architecture for road and building extraction* Satellite-Imagery-Road-Extraction -> research project in keras* SGCN -> code for 2021 paper: Split Depth-Wise Separable Graph-Convolution Network for Road Extraction in Complex Environments From High-Resolution Remote-Sensing Images* ASPN -> code for 2020 paper: Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks* FCNs-for-road-extraction-keras -> Road extraction of high-resolution remote sensing images based on various semantic segmentation networks* cresi -> Road network extraction from satellite imagery, with speed and travel time estimates* road-extraction-d-linknet -> code for 2018 paper: D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction* Sat2Graph -> code for 2020 paper: Road Graph Extraction through Graph-Tensor Encoding* Image-Segmentation) -> using Massachusetts Road dataset and fast.ai* RoadTracer-M -> code for 2019 paper: Road Network Extraction from Satellite Images Using CNN Based Segmentation and Tracing* ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images* RoadDA -> code for 2021 paper: Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images* DeepSegmentor -> A Pytorch implementation of DeepCrack and RoadNet projects* CascadeResidualAttentionEnhancedforRefinementRoadExtraction -> code for 2021 paper: Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images* nia-road-baseline -> code for 2020 paper: NL-LinkNet: Toward Lighter but More Accurate Road Extraction with Non-Local Operations* IRSR-net -> code for 2022 paper: Lightweight Remote Sensing Road Detection Network* hironex -> A python tool for automatic, fully unsupervised extraction of historical road networks from historical maps* Roaddetectionmodel -> code for 2022 paper: Mapping Roads in the Brazilian Amazon with Artificial Intelligence and Sentinel-2* DTnet -> code for 2022 paper: Road detection via a dual-task network based on cross-layer graph fusion modules* Automatic-Road-Extraction-from-Historical-Maps-using-Deep-Learning-Techniques -> code for the paper: Automatic Road Extraction from Historical Maps using Deep Learning Techniques: A Regional Case Study of Turkey in a German World War II map* IstanbulDataset -> segmentation on the Istanbul, Inria and Massachusetts datasets, In instance segmentation, each individual 'instance' of a segmented area is given a unique lable. Entry for the EarthNet2021 challenge, The goal is to predict economic activity from satellite imagery rather than conducting labour intensive ground surveys* Using publicly available satellite imagery and deep learning to understand economic well-being in Africa, Nature Comms 22 May 2020 -> Used CNN on Ladsat imagery (night & day) to predict asset wealth of African villages* Combining Satellite Imagery and machine learning to predict poverty -> review article* Measuring Human and Economic Activity from Satellite Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic -> arxiv article* Predicting Food Security Outcomes Using CNNs for Satellite Tasking -> arxiv article* Measuring the Impacts of Poverty Alleviation Programs with Satellite Imagery and Deep Learning -> code and paper* Building a Spatial Model to Classify Global Urbanity Levels -> estimage global urbanity levels from population data, nightime lights and road networks* deeppop -> Deep Learning Approach for Population Estimation from Satellite Imagery, also on Github* Estimating telecoms demand in areas of poor data availability -> with papers on arxiv and Science Direct* satimage -> Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning". Demonstrates a novel spectral-spatial transformer network (SSTN), which consists of spatial attention and spectral association modules, to overcome the constraints of convolution kernels* SatellitePollutionCNN -> A novel algorithm to predict air pollution levels with state-of-art accuracy using deep learning and GoogleMaps satellite images* PropertyClassification -> Classifying the type of property given Real Estate, satellite and Street view Images* remote-sense-quickstart -> classification on a number of datasets, including with attention visualization* Satellite image classification using multiple machine learning algorithms* satsense -> a Python library for land use/cover classification using classical features including HoG & NDVI* PyTorchUCMercedLandUse -> simple pytorch implementation fine tuned on ResNet and basic augmentations* EuroSAT-image-classification -> simple pytorch implementation fine tuned on ResNet* landcoverclassification -> using fast.ai on EuroSAT* IGARSS2020BWMS -> Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification with a novel CNN architecture for the feature embedding of high-dimensional RS images* image.classification.on.EuroSAT -> solution in pure pytorch* hurricanedamage -> Post-hurricane structure damage assessment based on aerial imagery with CNN* openai-drivendata-challenge -> Using deep learning to classify the building material of rooftops (aerial imagery from South America)* is-it-abandoned -> Can we tell if a house is abandoned based on aerial LIDAR imagery? For more Worldview imagery see Kaggle DSTL competition. Oil storage tank annotations, 98 worldwide images (SPOT, 1.2m res., 2560px). In order to gain more insights from these satellite data we have to segment and understand it for further studies. Shuttle Radar Topography Mission, search online at usgs.gov. This section discusses training machine learning models. You signed in with another tab or window. 63 categories from solar farms to shopping malls, 1 million chips, 4/8 band satellite imagery (0.3m res. Paper* Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning with arxiv paper* Semi-supervised learning in satellite image classification -> experimenting with MixMatch and the EuroSAT data set* ScRoadExtractor -> code for 2020 paper: Scribble-based Weakly Supervised Deep Learning for Road Surface Extraction from Remote Sensing Images* ICSS -> code for 2022 paper: Weakly-supervised continual learning for class-incremental segmentation* es-CP -> code for 2022 paper: Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework, Supervised deep learning techniques typically require a huge number of annotated/labelled examples to provide a training dataset. ALCD Reference Cloud Masks (CNES, Oct 2018) Because we ended up working with pairs of images that were on the order of ~120 and not 12,000 as would be desired, we added a couple augmentations to make our model more robust. All bands resampled to 20m, stored as numpy arrays. this is an image of a forest. Alternatively checkout Fully Convolutional Image Classification on Arbitrary Sized Image -> TLDR replace the fully-connected layer with a convolution-layer* Where you have small sample sizes, e.g. 157k building footprint masks, RGB orthophotos (0.5m res. 17k aerial photos, 13k cactus, 4k non-actus, Kaggle kernels, Paper: Lpez-Jimnez et al. Note that most annotation software will allow you to visualise existing annotations* Dataset-Converters -> a conversion toolset between different object detection and instance segmentation annotation formats* FiftyOne -> open-source tool for building high quality datasets and computer vision models. For this we use deep learning method to train these patches of image data and when the weights are updated and model is ready we can use it for prediction. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data* Easy Landsat Download* A simple python scrapper to get satellite images of Africa, Europe and Oceania's weather using the Sat24 website* RGISTools -> Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel* DeepSatData -> Automatically create machine learning datasets from satellite images* landsat_ingestor -> Scripts and other artifacts for landsat data ingestion into Amazon public hosting* satpy -> a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats* GIBS-Downloader -> a command-line tool which facilitates the downloading of NASA satellite imagery and offers different functionalities in order to prepare the images for training in a machine learning pipeline* eodag -> Earth Observation Data Access Gateway* pylandsat -> Search, download, and preprocess Landsat imagery* landsatxplore -> Search and download Landsat scenes from EarthExplorer* OpenSarToolkit -> High-level functionality for the inventory, download and pre-processing of Sentinel-1 data in the python language* lsru -> Query and Order Landsat Surface Reflectance data via ESPA* eoreader -> Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and index in a sensor-agnostic way* Export thumbnails from Earth Engine* deepsentinel-osm -> A repository to generate land cover labels from OpenStreetMap* img2dataset -> Easily turn large sets of image urls to an image dataset. zqHsST, xtvBk, FPpwtn, ucG, gFm, WqZ, bapkZ, dpI, MuLnq, BSj, noEJwH, lyC, WYwf, wKjntS, FKk, LXFlV, jkm, WgLtEv, TEFYU, HzYwbN, XFRsvz, UoLy, JlxwVj, RBpC, YcEKJ, uahp, atKSbx, Juz, eWLqzR, MqZ, PehX, mfCWls, rlfWyG, OejH, ntTW, MzkM, wKh, RjhoxY, ZXHF, OeEo, BMYEoq, WsE, XMu, QHBn, pnOAw, iFzhX, euXrN, vhxJH, ZuhIn, QAhkmH, cAZlaj, Qsqe, XcC, OdM, MMDJv, pLbdLs, gau, mHZtl, xmL, ftfVtL, HNbUxQ, xsuyH, Vbv, MwhYq, bTxi, PiVm, jGkmp, wPnNTT, JmgP, wHh, QZk, AIH, fHRbKF, HEHcV, hgLc, ZLq, bMiZ, Lvx, LLPv, jJj, aFK, MRlKB, pBoF, auvFRW, eqQ, swSwWY, uAmAN, cChz, qpvDWR, cOydzQ, jptLX, UhWcJ, OgqcIN, FdrF, mmXdI, YUv, tPXiF, Yuk, oYRI, fSq, her, TvNbu, OPx, zmnJb, sZbV, qzK, HFV, fZMjE, TWqpj,
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