Fortunately, this process is very easy and you should be approved almost immediately. "DIOR" is a large-scale benchmark dataset for object detection in optical remote sensing images, which consists of 23,463 images and 192,518 object instances annotated with horizontal bounding boxes ors-detection -> Object Detection on the DIOR dataset using YOLOv3 dior_detect -> benchmarks for object detection on DIOR dataset With paper* GF-CSL -> code for 2022 paper: Gaussian Focal Loss: Learning Distribution Polarized Angle Prediction for Rotated Object Detection in Aerial Images* simplifiedrboxcnn -> code for 2018 paper: RBox-CNN: rotated bounding box based CNN for ship detection in remote sensing image. * metaearth -> Download and access remote sensing data from any platform* geoget -> Download geodata for anywhere in Earth via ladsweb.modaps.eosdis.nasa.gov* geeml -> A python package to extract Google Earth Engine data for machine learning. However, this should be helpful for any cases that involve using public satellite data for image models. Read my blog post A brief introduction to satellite image segmentation with neural networks* awesome-satellite-images-segmentation* Satellite Image Segmentation: a Workflow with U-Net is a decent intro article * mmsegmentation -> Semantic Segmentation Toolbox with support for many remote sensing datasets including LoveDA, Potsdam, Vaihingen & iSAID* segmentationgym -> A neural gym for training deep learning models to carry out geoscientific image segmentation* 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 -> python code to blend predicted patches smoothly. 13 different spectrum for multi spectral dataset <> Screenshot from EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification. web pages Some of these tools are simply for performing annotation, whilst others add features such as dataset management and versioning. Also checkout Multi-label Land Cover Classification using the redesigned multi-label Merced dataset with 17 land cover classes * Multi-Label Classification of Satellite Photos of the Amazon Rainforest using keras or FastAI * Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier with repo* Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset* Land Cover Classification of Satellite Imagery using Convolutional Neural Networks using Keras and a multi spectral dataset captured over vineyard fields of Salinas Valley, California* Detecting deforestation from satellite images -> using FastAI and ResNet50, with repo fsdldeforestationdetection* Neural Network for Satellite Data Classification Using Tensorflow in Python -> A step-by-step guide for Landsat 5 multispectral data classification for binary built-up/non-built-up class prediction, with repo* Slums mapping from pretrained CNN network on VHR (Pleiades: 0.5m) and MR (Sentinel: 10m) imagery* Comparing urban environments using satellite imagery and convolutional neural networks -> includes interesting study of the image embedding features extracted for each image on the Urban Atlas dataset. "minimum 50% overlap"* For more comprehensive definitions checkout Object-Detection-Metrics* Metrics to Evaluate your Semantic Segmentation Model, This section includes tips and ideas I have picked up from other practitioners including ai-fast-track, FraPochetti & the IceVision community* Almost all imagery data on the internet is in RGB format, and common techniques designed for working with this 3 band imagery may fail or need significant adaptation to work with multiband data (e.g. Follow the instructions to sign up here. But evaluation of other single band can also be done, where we can use images as input consisting the information observed from a single spectrum on all three input channels. ), 4 global cities, 1 holdout city for leaderboard evaluation, APLS metric, baseline model, SEN12MS (TUM, Jun 2019) In the same article they propose using semantic segmentation combined with a CNN for a cloud classifier (excellent review paper here), but state that this requires too much compute resources. ), 5 cities, SpaceNet Challenge Asset Library, SpaceNet 1: Building Detection v1 (CosmiQ Works, Radiant Solutions, NVIDIA, Jan 2017) Proposes and demonstrates a new architecture with perturbation layers with practical guidance on the methodology and code. Includes clear, cloud and cloud-shadow classes. The mapping problems include road network inference, building footprint extraction, etc. In this blog we try to use deep learning methods to work with land cover classification on EuroSAT dataset. 2021, NEON Tree Crowns Dataset (Weinstein et al., 2020) Satellite images of different spectrum is taken through years and stored, when these type of data is accessible and are labelled then it can be used for further studies. Semi-supervised semantic segmentation, 19 cities and surroundings with multi-sensor tiles (VHR Aerial imagery 50cm res., Elevation model) & per pixel labels (contains landcover / landuse classes from UrbanAtlas 2012), Data. You can change this in javascript code. * ImageRegistration -> Interview assignment for multimodal image registration using SIFT* imregdft -> Image registration using discrete Fourier transform. A GPU is required for training deep learning models (but not necessarily for inferencing), and this section lists a couple of free Jupyter environments with GPU available. The correct choice of metric is particularly critical for imbalanced dataset problems, e.g. Applying machine learning skills on geography has been a trending field. NeRF stands for Neural Radiance Fields and is the term used in deep learning communities to describe a model that generates views of complex 3D scenes based on a partial set of 2D images* Wikipedia DEM article and phase correlation article* Intro to depth from stereo* Map terrain from stereo images to produce a digital elevation model (DEM) -> high resolution & paired images required, typically 0.3 m, e.g. The list is now archived. List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. 2019, Open AI Challenge: Caribbean (MathWorks, WeRobotics, Wordlbank, DrivenData, Dec 2019) I sometimes write too. In recent years we have seen a rapid growth in the field of machine learning and artificial intelligence. ), Amazonian rainforest, Kaggle kernels, AID: Aerial Scene Classification (Xia et al., 2017) We use transfer learning, here we use wide_resnet50_2 model as a pretrained model which is already trained on a huge image dataset. Paper: I will describe in a relatively technical manner (code included) how to quickly download some satellite images from the google earth engine and then use them to train a 3-dimensional Convolutional Neural Network. Visualise labels, evaluate model predictions, explore scenarios of interest, identify failure modes, find annotation mistakes, and much more! Some points about the EuroSAT dataset is described below. 13 land cover categories + 4 cloud condition categories, 4-band (RGB-NIR) satelitte imagery (5m res. Contrary to what you would expect with regular Feedforward Networks (see this nice StackExchange discussion for some background theory), there is no consensus as to how deep to make your CNNs. @0.5 sets a threshold for how much of the predicted bounding box overlaps the ground truth bounding box, i.e. 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". These land cover changes can be used for various studies and purposes. * Intel to place movidius in orbit to filter images of clouds at source - Oct 2020 - Getting rid of these images before theyre even transmitted means that the satellite can actually realize a bandwidth savings of up to 30%* Whilst not involving neural nets the PyCubed project gets a mention here as it is putting python on space hardware such as the V-R3x* WorldFloods will pioneer the detection of global flood events from space, launched on June 30, 2021. ), SpaceNet Challenge Asset Library. Instead, we turned to Google Earth Engine, which could filter by date, crop, display cloud density and provide download links all at the click of a button! We create a EuroSAT dataset class inherited from torch dataset library. * How not to test your deep learning algorithm? ), Paper: Mohajerani et al. 2019. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. AttUnet, MobileNetUnet, NestedUNet, R2AttUNet, R2UNet, SEUnet, scSEUnet, UnetXceptionResNetBlock, in keras* Efficient-Transformer -> code for 2021 paper: Efficient Transformer for Remote Sensing Image Segmentation* weaklysupervised -> code for the 2020 paper: Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery* HRCNet-High-Resolution-Context-Extraction-Network -> code to 2021 paper: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images* Semantic segmentation of SAR images using a self supervised technique* satellite-segmentation-pytorch -> explores a wide variety of image augmentations to increase training dataset size* IEEETGRSSpectralFormer -> code for 2021 paper: Spectralformer: Rethinking hyperspectral image classification with transformers* Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels -> code for 2022 paper* Semantic-Segmentation-with-Sparse-Labels -> codes and data for learning from sparse annotations* SNDF -> code for 2020 paper: Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation* Satellite-Image-Classification -> using random forest or support vector machines (SVM) and sklearn* dynamic-rs-segmentation -> code for 2019 paper: Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks* Remote-sensing-image-semantic-segmentation-tf2 -> remote sensing image semantic segmentation repository based on tf.keras includes backbone networks such as resnet, densenet, mobilenet, and segmentation networks such as deeplabv3+, pspnet, panet, and refinenet* segmentationmodels.pytorch -> Segmentation models with pretrained backbones, has been used in multiple winning solutions to remote sensing competitions* SSRN -> code for 2017 paper: Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework* SO-DNN -> code for 2021 paper: Simplified object-based deep neural network for very high resolution remote sensing image classification* SANet -> code for 2019 paper: Scale-Aware Network for Semantic Segmentation of High-Resolution Aerial Images* aerial-segmentation -> code for 2017 paper: Learning Aerial Image Segmentation from Online Maps* IterativeSegmentation -> code for 2016 paper: Recurrent Neural Networks to Correct Satellite Image Classification Maps* Detectron2 FPN + PointRend Model for amazing Satellite Image Segmentation -> 15% increase in accuracy when compared to the U-Net model* HybridSN -> code for 2019 paper: HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification. Landsat, one of the publicly available satellite image datasets, gives you 30 meters resolution and you get one picture every 14 days. For convenience they are all listed here:* cownterstrike -> counting cows, located with point-annotations, two models: CSRNet (a density-based method) & LCFCN (a detection-based method)* elephantdetection -> Using Keras-Retinanet to detect elephants from aerial images* CNN-Mosquito-Detection -> determining the locations of potentially dangerous breeding grounds, compared YOLOv4, YOLOR & YOLOv5* BorowiczetalSpacewhale -> locate whales using ResNet* walrus-detection-and-count -> uses Mask R-CNN instance segmentation* MarineMammalsDetection -> Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images* Audubon_F21 -> code for 2022 paper: Deep object detection for waterbird monitoring using aerial imagery, Oil is stored in tanks at many points between extraction and sale, and the volume of oil in storage is an important economic indicator. Patches of images are extracted out and stored for various studies. ), pre-trained baseline model. building flooded, building non-flooded, road-flooded, ..), 2 competition tracks (Binary & semantic flood classification; Object counting & condition recognition), Dynamic EarthNet Challenge (Planet, DLR, TUM, April 2021) ), Kaggle kernels, SPARCS: S2 Cloud Validation data (USGS, 2016) These processes may be referred to as Human-in-the-Loop Machine Learning* Active learning for object detection in high-resolution satellite images -> arxiv paper* AIDE V2 - Tools for detecting wildlife in aerial images using active learning* AstronomicAL -> An interactive dashboard for visualisation, integration and classification of data using Active Learning* Read about active learning on the lightly platform and in label-studio* Active-Labeler by spaceml-org -> a CLI Tool that facilitates labeling datasets with just a SINGLE line of code* Labelling platform for Mapping Africa active learning project* ChangeDetectionProject -> Trying out Active Learning in with deep CNNs for Change detection on remote sensing data* ALS4GAN -> Active Learning for Improved Semi Supervised Semantic Segmentation in Satellite Images, with paper* Active-Learning-for-Remote-Sensing-Image-Retrieval -> unofficial implementation of paper: A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval* DIAL -> code for 2022 paper: DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing, Federated learning is a process for training models in a distributed fashion without sharing of data* Federated-Learning-for-Remote-Sensing -> implementation of three Federated Learning models, Image registration is the process of registering one or more images onto another (typically well georeferenced) image. Its use is controversial since it can introduce artefacts at the same rate as real features. However, the performance is unsatisfactory because low clouds and sea fog are hard to distinguish on satellite images because they have similar spectral radiance characteristics. Paper: Chiu et al. [1] S. L. Ullo, M.S. 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. 2020, IEEE Data Fusion Contest 2022 (IEEE GRSS, Universit Bretagne-Sud, ONERA, ESA, Jan 2022) 1980 image chips of 256 x 256 pixels in V1.0 spanning 66 tiles of Sentinel-2.
Satellite Image Classification | Kaggle Modified + productionized model based off the first-place model from the xView2 challenge. 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. 2019, DEEPGLOBE - 2018 Satellite Challange (CVPR, Apr 2018) This satellite images can be used for classification. ), USDA Cropland Data Layer as ground truth. Please see these fantastic ressources for more recent datasets: Airbus Ship Detection Challenge (Airbus, Nov 2018) 2014, Biome: L8 Cloud Cover Validation data (USGS, 2016) Quick note: you will have to sign up to become a google earth engine developer to access the console that we used. Also checkout this implementation in Jax* Super Resolution in OpenCV* AI-based Super resolution and change detection to enforce Sentinel-2 systematic usage -> Worldview-2 images (2m) were used to create a reference dataset and increase the spatial resolution of the Copernicus sensor from 10m to 5m* SRCDNet -> The pytorch implementation for "Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions ". 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. Active learning techniques aim to reduce the total amount of annotation that needs to be performed by selecting the most useful images to label from a large pool of unlabelled images, thus reducing the time to generate useful training datasets. JPEG? 10 land cover classes, temporal stack of hyperspectral Sentinel-2 imagery (R,G,B,NIR,SWIR1,SWIR2; 10 m res.) Ref* The false positive rate (FPR), calculated as FPR = FP/(FP+TN) is often plotted against recall/TPR in an ROC curve which shows how the TPR/FPR tradeoff varies with classification threshold. 96469649. 8 classes (inc. cloud and cloud shadow) for 38 Sentinel-2 scenes (10 m res.). Slovenia Land Cover Classification (Sinergise, Feb 2019) subset Landsat 8 scenes (30m res. Dataset Some points about the EuroSAT dataset is described below. ), Rotterdam, Netherlands.
Atmosphere | Free Full-Text | Deep Convolutional Neural Networks With additional pre-processing image rotation and scale changes can also be calculated. Under the guidance of Silvia Ullo and with some help from her graduate students Maria Pia Del Rosso, Alejandro Sebastianelli, and Federico Picarillo, we ended up submitting a paper[1] to a remote sensing conference! BigEarthNet: Large-Scale Sentinel-2 Benchmark (TU Berlin, Jan 2019) Used in the 2022 paper: Improved YOLOv5 network method for remote sensing image-based ground objects recognition* oilstorage-detector -> using yolov5 and the Airbus Oil Storage Detection dataset* oilwell_detector -> detect oil wells in the Bakken oil field based on satellite imagery* OGST -> Oil and Gas Tank Dataset* AContrarioTankDetection -> code for 2020 paper: Oil Tank Detection in Satellite Images via a Contrario Clustering* SubpixelCircleDetection -> code for 2020 paper: CIRCULAR-SHAPED OBJECT DETECTION IN LOW RESOLUTION SATELLITE IMAGES* Oil Storage Detection on Airbus Imagery with YOLOX -> uses the Kaggle Airbus Oil Storage Detection dataset, There are many algorithms that use band math to detect clouds, but the deep learning approach is to use semantic segmentation* CloudSEN12 -> Sentinel 2 cloud dataset with a varierty of models here* See section Kaggle - Understanding Clouds from Satellite Images* From this article on sentinelhub there are three popular classical algorithms that detects thresholds in multiple bands in order to identify clouds.
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