Satellite image download

Author: e | 2025-04-24

Different with Google Satellite Maps Downloader, Google Satellite Maps Downloader downloads satellite images from Google Maps, but google earth images downloader downloads images Automatically download Bing satellite maps images. AllMapSoft Yahoo Satellite Maps Downloader 6.602. Automatically download Yahoo satellite maps images.

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With 700 images in each class.eurosat - EuroSAT dataset is based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples.bigearthnet - The BigEarthNet is a new large-scale Sentinel-2 benchmark archive, consisting of 590,326 Sentinel-2 image patches. The image patch size on the ground is 1.2 x 1.2 km with variable image size depending on the channel resolution. This is a multi-label dataset with 43 imbalanced labels.AWS datasetsEarth on AWS is the AWS equivalent of Google Earth EngineCurrently 36 satellite datasets on the Registry of Open Data on AWSMicrosoftUSBuildingFootprints -> computer generated building footprints in all 50 US states, GeoJSON format, generated using semantic segmentationCheckout Microsofts Planetary Computer projectGoogle Earth Engine (GEE)Since there is a whole community around GEE I will not reproduce it here but list very select references. Get started at imagery and climate datasets, including Landsat & Sentinel imageryawesome-google-earth-engine & awesome-earth-engine-appsHow to Use Google Earth Engine and Python API to Export Images to Roboflow -> to acquire training dataReduce Satellite Image Resolution with Google Earth Engine -> a crucial step before applying machine learning to satellite imageryee-fastapi is a simple FastAPI web application for performing flood detection using Google Earth Engine in the backend.How to Download High-Resolution Satellite Data for Anywhere on EarthRadiant Earth and also models on ‘world’s largest satellite image database’Database of 15,000 high-definition images with 1 million labelled ‘scenes’ will be open to the international community in June 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing ImageryDEM (digital elevation maps)Shuttle Radar Topography Mission: data - open accessCopernicus Digital Elevation Model (DEM) on S3, represents the surface of the Earth including buildings, infrastructure and vegetation. Data is provided as Cloud Optimized GeoTIFFs. linkWeather DatasetsUK metoffice -> (make request and emailed when ready) -> (requires BigQuery) -> series weather data for several US cities -> series & change detection datasetsBreizhCrops -> A Time Series Dataset for Crop Type MappingThe SeCo dataset contains image patches from Sentinel-2 tiles captured at different timestamps at each geographical location. Download SeCo hereOnera Satellite Change Detection Dataset comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018SYSU-CD -> The dataset contains 20000 pairs of 0.5-m aerial images of size 256×256 taken between the years 2007 and 2014 in Hong KongUAV & Drone datasetsMany on dataset -> a multi-modal UAV dataset for object detection.ERA

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The average image is 1 to 3 years old. I believe they all got that figure from an old post on the Google Earth blog that has since been deleted.Table of ContentsClick on the images to enlarge them.1. Download and Install Google Earth ProThe first thing you should do in order to see a satellite image of your house is download and install Google Earth Pro.You can use Google Earth on the web without installing it if you’d like, but Google Earth Pro has features you may want later, and…it’s free. You can download Google Earth Pro here.2. Open Google EarthLaunch Google Earth Pro (or Google Earth).If this is the first time you’ve opened it, you’ll see a satellite image composite of the Earth with menus and tools around the sides of the screen, with a “Start-up Tips” box blocking the middle of the screen.Close the “Start-up Tips” box.3. Search for the Address Where You Want to See a Satellite ImageIn the top left corner of the screen, you’ll see a search field.Start entering the address of the house (or any other building!) that you want to see.Google Earth will offer suggested addresses below the search field as you type. When you see yours, click on it and it will appear in the search box.Or just type in the full address.4. Watch as You Zoom to the LocationOnce the correct address is in the box, click “Search”. Google Earth will now zoom you to that location.5. Look for the Orange

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RGB) and 16-band (400nm - SWIR) images10 Labelled classes include - Buildings, Road, Trees, Crops, Waterway, VehiclesInterview with 1st place winner who used segmentation networks - 40+ models, each tweaked for particular target (e.g. roads, trees)Deepsense 4th place solutionEntry by lopuhin using UNet with batch-normalizationKaggle - Airbus Ship Detection Challenge - medium, most solutions using deep-learning, many kernels, good example kernelI believe there was a problem with this dataset, which led to many complaints that the competition was ruinedKaggle - Draper - place images in order of time - hard. Not many useful kernels.Images are grouped into sets of five, each of which have the same setId. Each image in a set was taken on a different day (but not necessarily at the same time each day). The images for each set cover approximately the same area but are not exactly aligned.Kaggle interviews for entrants who used XGBOOST and a hybrid human/ML approachKaggle - Deepsat - classification challengeNot satellite but airborne imagery. Each sample image is 28x28 pixels and consists of 4 bands - red, green, blue and near infrared. The training and test labels are one-hot encoded 1x6 vectors. Each image patch is size normalized to 28x28 pixels. Data in .mat Matlab format. JPEG?Imagery sourceSat4 500,000 image patches covering four broad land cover classes - barren land, trees, grassland and a class that consists of all land cover classes other than the above threeSat6 405,000 image patches each of size 28x28 and covering 6 landcover classes - barren land, trees, grassland, roads, buildings and water bodies.Deep Gradient Boosted Learning articleKaggle - Understanding Clouds from Satellite ImagesIn this challenge, you will build a model to classify cloud organization patterns from satellite images. place solution on Github by naivelambKaggle - Airbus oil storage detection dataset Tank Instance Segmentation with Mask R-CNN with accompanying articleKaggle - Satellite images of hurricane damage - miscellaneous -> Satellite + loan data -> Image data of industrial tanks with bounding box annotations, estimate tank fill % from shadows -> Classify ships in San Franciso Bay using Planet satellite imagery -> Detect aircraft in Planet satellite image chips -> A Benchmark Satellite Dataset as Drop-In Replacement for MNIST -> Land Cover Classification Dataset from DeepGlobe ChallengeTensorflow datasetsresisc45 - RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes. Different with Google Satellite Maps Downloader, Google Satellite Maps Downloader downloads satellite images from Google Maps, but google earth images downloader downloads images

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Images into tiles and merge tiles into a large imagefelicette -> Satellite imagery for dummies. Generate JPEG earth imagery from coordinates/location name with publicly available satellite data.imagehash -> Image hashes tell whether two images look nearly identical.xbatcher -> Xbatcher is a small library for iterating xarray DataArrays in batches. The goal is to make it easy to feed xarray datasets to machine learning libraries such as Keras.fake-geo-images -> A module to programmatically create geotiff images which can be used for unit testssahi -> A vision library for performing sliced inference on large images/small objectsimagededup -> Finding duplicate images made easy! Uses perceptual hashingrmstripes -> Remove stripes from images with a combined wavelet/FFT approachactiveloopai Hub -> The fastest way to store, access & manage datasets with version-control for PyTorch/TensorFlow. Works locally or on any cloud. Scalable data pipelines.sewar -> All image quality metrics you need in one packagefiftyone -> open-source tool for building high-quality datasets and computer vision models. Visualise complex labels, evaluating models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more!GeoTagged_ImageChip -> A simple script to create geo tagged image chips from high resolution RS iamges for training deep learning models such as Unet.Image augmentation packagesImage augmentation is a technique used to expand a training dataset in order to improve ability of the model to generaliseAugLy -> A data augmentations library for audio, image, text, and video. By Facebookalbumentations -> Fast image augmentation library and an easy-to-use wrapper around other librariesFoHIS -> Towards Simulating Foggy and Hazy Images and Evaluating their AuthenticityDeep learning packagesrastervisiontorchvision-enhance -> Enhance PyTorch vision for semantic segmentation, multi-channel images and TIF fileDeepHyperX -> A Python/pytorch tool to perform deep learning experiments on various hyperspectral datasets.Data discovery and ingestionlandsat_ingestor -> Scripts and other artifacts for landsat data ingestion into Amazon public hostingsatpy -> a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formatsGIBS-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 pipelineeodag -> Earth Observation Data Access Gatewaypylandsat -> Search, download, and preprocess Landsat imagerysentinelsat -> Search and download Copernicus Sentinel satellite imageslandsatxplore -> Search and download Landsat scenes from EarthExplorerGraphing and visualisationhvplot -> A high-level plotting API for the PyData ecosystem built on HoloViews. Allows overlaying data on map tiles,

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Weather Satellite Images: Indonesia Weather satellite images (Indonesia) show the cloud cover. New satellite observations become available every 5 to 15 minutes, depending on the location. The images can be animated to produce a minute-by-minute satellite view of the weather. The satellite animation is a great tool to understand weather development and movement of clouds, and is often used by meteorologists for short term weather forecasting. The global satellite composite is generated from 5 different satellites (METEOSAT, GOES-16, GOES-17, HIMAWARI, METEOSAT-IODC) and processed into an Earth-colour image for better readability. The global satellite image has the maximum possible resolution as provided by the satellites, yielding an incredible 500 megapixels for the entire world. Why are there some clouds missing at night? During daytime the satellite can take high resolution photos of the weather using the wavelengths of visible light. But unlike your digital camera, the satellite can also take pictures at night, using infrared radiation. This thermal infrared measures the temperature of objects, and cold objects appear in a bright white. Therefore, cold clouds appear very bright while warm clouds are less visible. Low clouds, and especially fog, do sometimes have similar temperatures as the Earth’s surface and then become almost invisible to the satellite at night. As the infrared signal is much weaker than visible light, the resolution of satellite images is much less at night than during daytime. Why can I not see my house on the satellite image? The weather satellites need to take a picture of the entire world every 5 to 10 minutes. In order for this to work they have to be far away (at ~36'000 km altitude). At this distance your house is simply too small to be visible. The satellite image you know from e.g. Google maps was taken from only 100

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Architecture with perturbation layers with practical guidance on the methodology and code. Three part seriesSuper Resolution for Satellite Imagery - srcnn repoTensorFlow implementation of "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" adapted for working with geospatial dataRandom Forest Super-Resolution (RFSR repo) including sample dataSuper-Resolution (python) Utilities for managing large satellite imagesEnhancing Sentinel 2 images by combining Deep Image Prior and Decrappify. Repo for deep-image-prior and article on decrappifyThe keras docs have a great tutorial - Image Super-Resolution using an Efficient Sub-Pixel CNNHighRes-net -> Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agency’s Kelvin competitionsuper-resolution-using-gan -> Super-Resolution of Sentinel-2 Using Generative Adversarial NetworksSuper-resolution of Multispectral Satellite Images Using Convolutional Neural Networks with paperSmall-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network -> enhanced super-resolution GAN (ESRGAN)pytorch-enhance -> Library of Image Super-Resolution Models, Datasets, and Metrics for Benchmarking or Pretrained Use. Also checkout this implementation in JaxMulti-temporal Super-Resolution on Sentinel-2 Imagery using HighRes-Net, repoimage-super-resolution -> Super-scale your images and run experiments with Residual Dense and Adversarial Networks.SSPSR-Pytorch -> A spatial-spectral prior deep network for single hyperspectral image super-resolutionSentinel-2 Super-Resolution: High Resolution For All (Bands)super-resolution for satellite images using SRCNNCinCGAN -> Unofficial Implementation of Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial NetworksSatellite-image-SRGAN using PyTorchSuper Resolution in OpenCVdeepsum -> Deep neural network for Super-resolution of Unregistered Multitemporal images (ESA PROBA-V challenge)3DWDSRNet -> code to reproduce Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural NetworksImage-to-image translationTranslate images e.g. from SAR to RGB.How to Develop a Pix2Pix GAN for Image-to-Image Translation -> how to develop a Pix2Pix model for translating satellite photographs to Google map images. A good intro to GANSSAR to RGB Translation using CycleGAN -> uses a CycleGAN model in the ArcGIS API for PythonA growing problem of ‘deepfake geography’: How AI falsifies satellite imagesKaggle Pix2Pix Maps -> dataset for pix2pix to take a google map satellite photo and build a street mapguided-deep-decoder -> With guided deep decoder, you can solve different image pair fusion problems, allowing super-resolution, pansharpening or denoisingSARRemoving speckle noise from Sentinel-1 SAR using a CNNA dataset which is specifically made for deep learning on SAR and optical imagery is the SEN1-2 dataset, which contains corresponding patch pairs of Sentinel 1 (VV) and 2 (RGB) data. It is the largest manually curated dataset of S1 and S2 products, with corresponding labels for land use/land cover. Different with Google Satellite Maps Downloader, Google Satellite Maps Downloader downloads satellite images from Google Maps, but google earth images downloader downloads images