Displaying 1 to 20 from 22 results

sentinelhub-py - Download and process satellite imagery in Python using Sentinel Hub services.

  •    Python

The sentinelhub Python package allows users to make OGC (WMS and WCS) web requests to download and process satellite images within your Python scripts. It supports Sentinel-2 L1C and L2A, Sentinel-1, Landsat 8, MODIS and DEM data source. The package also supports obtaining data from Amazon Web Service. It can either provide data from public bucket with Sentinel-2 L1C imagery or requester pays bucket with Sentinel-2 L2A imagery. If specified the downloaded data can be stored in ESA .SAFE format (all types of .SAFE format are supported).

sentinelsat - Search and download Copernicus Sentinel satellite images

  •    Python

Sentinelsat makes searching, downloading and retrieving the metadata of Sentinel satellite images from the Copernicus Open Access Hub easy. and a powerful Python API.

satellite-image-deep-learning - Resources for performing deep learning on satellite imagery

  •    Jupyter

This document primarily lists resources for performing deep learning (DL) on satellite imagery. To a lesser extent Machine learning (ML, e.g. random forests, stochastic gradient descent) are also discussed, as are classical image processing techniques. Kaggle hosts several large satellite image datasets (> 1 GB). A list if general image datasets is here. A list of land-use datasets is here. The kaggle blog is an interesting read.

TernausNetV2 - TernausNetV2: Fully Convolutional Network for Instance Segmentation

  •    Jupyter

We present network definition and weights for our second place solution in CVPR 2018 DeepGlobe Building Extraction Challenge. Automatic building detection in urban areas is an important task that creates new opportunities for large scale urban planning and population monitoring. In a CVPR 2018 Deepglobe Building Extraction Challenge participants were asked to create algorithms that would be able to perform binary instance segmentation of the building footprints from satellite imagery. Our team finished second and in this work we share the description of our approach, network weights and code that is sufficient for inference.

gfw - Global Forest Watch: An online, global, near-real time forest monitoring tool

  •    HTML

Global Forest Watch (GFW) is a dynamic online forest monitoring and alert system that empowers people everywhere to better manage forests. This repository contains the GFW web app.

sentinel-util - A CLI for downloading, processing, and making a mosaic from Sentinel-1, -2 and -3 data

  •    Python

Sentinel Util is a command line utility to create a mosaic from Sentinel images. out The folder where the image file will be written (i.e. /path/to/your/folder).

robosat - Semantic segmentation on aerial and satellite imagery

  •    Python

RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. Have a look at this OpenStreetMap diary post where we first introduced RoboSat and show some results.

satellite-image-object-detection - YOLO/YOLOv2 inspired deep network for object detection on satellite images (Tensorflow, Numpy, Pandas)

  •    Python

The dataset is/was available on https://www.datasciencechallenge.org/challenges/1/safe-passage/ . preprocess.py lets you transform the 2000x2000 images into 250x250 images and a CSV file with all the objects annotations. The dataset contains only the position of the center of the objects (no bounding boxes). A bounding box is generated. It's just a square centered on the provided position (x,y). The size of the square varies depending on the type of vehicle. We're using 8 object classes: Motorcycle, Light short rear, Light long rear, Dark short rear, Dark long rear, Red short rear, Red long rear, Light van. Other types of vehicles are ignored.

MODIStsp - An "R" package for automatic download and preprocessing of MODIS Land Products Time Series

  •    R

MODIStsp is a “R” package devoted to automatizing the creation of time series of rasters derived from MODIS Land Products data. MODIStsp allows to perform several preprocessing steps (e.g., download, mosaicing, reprojection and resize) on MODIS data available within a given time period. Users have the ability to select which specific layers of the original MODIS HDF files they want to process. They also can select which additional Quality Indicators should be extracted from the aggregated MODIS Quality Assurance layers and, in the case of Surface Reflectance products, which Spectral Indexes should be computed from the original reflectance bands. For each output layer, outputs are saved as single-band raster filescorresponding to each available acquisition date. Virtual files allowing access to the entire time series as a single file can be also created. All processing parameters can be easily selected with a user-friendly GUI, although non-interactive execution exploiting a previously created Options File is possible. Stand-alone execution outside an “R” environment is also possible, allowing to use scheduled execution of MODIStsp to automatically update time series related to a MODIS product and extent whenever a new image is available. L. Busetto, L. Ranghetti (2016) MODIStsp: An R package for automatic preprocessing of MODIS Land Products time series, Computers & Geosciences, Volume 97, Pages 40-48, ISSN 0098-3004, http://dx.doi.org/10.1016/j.cageo.2016.08.020, URL: https://github.com/ropensci/MODIStsp.

gnd-android - Ground for Android

  •    Java

Ground is a map-centric data collection platform for occasionally connected devices. This is not an officially supported Google product; it is currently being developed by volunteers on a best-effort basis.

ground-android - Ground mobile data collection app for Android

  •    Java

Ground is a free, map-centric data collection platform for occasionally connected devices. This is not an officially supported Google product; it is currently being developed by volunteers on a best-effort basis.

notebooks - interactive notebooks from Planet Engineering

  •    Jupyter

In this repository, you'll find a collection of Jupyter notebooks from the software developers, data scientists, and developer advocates at Planet. These interactive, open-source (APLv2) guides are designed to help you explore Planet data, work with our APIs and tools, and learn how to extract information from our massive archive of high-cadence satellite imagery. We hope these guides will inspire you to ask interesting questions of Planet data. Need help? Find a bug? Please file an issue and we'll get back to you. Soon we hope to add notebooks from the researchers, technologists, geographers, and entrepreneurs who are already using Planet data to ask interesting and innovative questions about our changing Earth. If you're working with our imagery and have a notebook (or just an idea for a notebook) that you'd like to share, please file an issue and let us know.

imagery-index - 🛰 An index of aerial and satellite imagery useful for mapping

  •    Javascript

🛰 An index of aerial and satellite imagery useful for mapping. 👉 See CONTRIBUTING.md for full details about how to add an imagery source to this index.

Danesfield - Kitware's system for 3D building reconstruction for the IARPA CORE3D program

  •    Python

This repository addresses the algorithmic challenges of the IARPA CORE3D program. The goal of this software is to reconstruct semantically meaningful 3D models of buildings and other man-made structures from satellite imagery. This repository contains the algorithms to solve the CORE3D problem, but the user interface and cloud-based processing infrastructure are provided in a separate project called Resonant Geo. The algorithms in this repository are written in Python or at least provide a Python interface.

sentinelloader - Sentinel-2 satellite tiles images downloader from Copernicus

  •    Jupyter

Sentinel-2 satellite tiles images downloader from Copernicus. With this utility you can specify the desired polygon, image resolution, band name and aproximate dates and it will do the best effort to find all tiles needed to satisfy your requirement. Then it will download minimal data by selecting just the needed .jp2 files inside Products, combine downloaded tiles, crop the combined tiles image to the polygon and cache the results, returning a GeoTIFF image with raster for the selected area.

aerialbot - A simple yet highly configurable bot that tweets geotagged aerial imagery of a random location in the world

  •    Python

A simple yet highly configurable bot that tweets geotagged aerial imagery of a random location in the world. In a bit more detail, whenever you run ærialbot, it...

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