Techniques for deep learning with satellite & aerial imagery
-
Updated
May 28, 2024
Techniques for deep learning with satellite & aerial imagery
A python package that extends Google Earth Engine.
AiTLAS implements state-of-the-art AI methods for exploratory and predictive analysis of satellite images.
🧠 Applying the Tile2vec model to the Eurosat dataset in the multispectral variant
Remote Sensing & Satellite Imagery - CMP2024 - Computer Engineering - Cairo University
A QGIS plugin tool using Segment Anything Model (SAM) to accelerate segmenting or delineating landforms in geospatial raster images.
Image and Telemetry decoder for some amateurs satellites (geoscan, sputnix platforms...)
Collect timeseries multispectral satellite images using the Sentinel Hub API
Spatiotemporal Arrays, Raster and Vector Data Cubes
ASP.NET Core-based viewer for captured NOAA satellite images
Click below to checkout the website
Global shoreline mapping tool from satellite imagery
[NeurIPS 2023] Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
Dealing with Multiplatform Satellite Images from Landsat, MODIS, and Sentinel
Using Deep Learning in satellite Imagery
Implementation of image classification of satellite images
Awesome projects, papers, and tools for working with hyperspectral imagery
CNN for predicting crop yield
Analysis and classification of satellite images. A novel dataset and deep learning benchmark for land use and land cover classification (Eurosat)
This repository shows how to get satellite images to build a dataset to train a neural network. It use the MiniFrance land cover dataset, Google-Earth-Engine to download satellite images, and Pytorch to train a neural network.
Add a description, image, and links to the satellite-images topic page so that developers can more easily learn about it.
To associate your repository with the satellite-images topic, visit your repo's landing page and select "manage topics."