🛰️ List of satellite image training datasets with annotations for computer vision and deep learning

Satellite Image Classification using semantic segmentation methods in deep learning

This page presents a list of satellite imagery datasets with a temporal dimension, mainly satellite image time series (SITS) and satellite videos, for various computer vision and deep learning tasks. It covers multi-temporal datasets with more than two acquisitions but not bi-temporal datasets.

crop classification using deep learning on satellite images

A deep learning model for surface water mapping based on satellite optical image.

This includes short and minimalistic few examples covering fundamentals of Deep Learning for Satellite Image Analysis (Remote Sensing).

Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the earth from June, 2015. This image patches can be trained and classified using transfer learning techniques.

Deep learning courses and projects

Research project on building and evaluating deep learning models for landslides detection on satellite images

This is a Semester Project which aim is to implement a Deep Learning model in order to detect Flood Events from Satellite Images

A deep learning approach for mapping and dating burned areas using temporal sequences of satellite images

Code and models for the manuscript "Predicting Poverty and Developmental Statistics from Satellite Images using Multi-task Deep Learning"

🐳 EuroSAT🐊 Satellite 🐲 Image 🪲 Land 🎳 Use 🌳 Classification 🐸 is a 🪆 computer 🐧vision 🦫 that 🦧 leverages 🦊 Deep 🐯 Learning 🍊and CNN 🥯 architectures 🍏 to classify 🥭 satellite 🍔 images 🧅into ✈ different 🚞 land use 🚒 land cover 🕌 categories 🚁 Built 🎮 on the 🛼 EuroSAT 🛬 dataset ⚾ this 🛸showcases ⚽ environmental 🏀monitoring 🏈

A deep learning (neural network) land cover classification project using satellite images (remote sensing).

Developed a novel algorithm to predict air pollution levels with state-of-art accuracy using deep learning and GoogleMaps satellite images

Tree detection from satellite image using deep learning for the final project of our school

Analysis satellite images of typhoons in deep-learning (CNN).

Practical Project for Semantic Segmentation of Building Footprint from Satellite Images

In this project, I developed a Pix2Pix generative adversarial network for image-to-image translation. I have used the so-called maps dataset used in the Pix2Pix paper.

In this project, we detect forest wildfire from given satellite images using deep learning.

Mapping the extent of land use and land cover categories over time is essential for better environmental monitoring, urban planning and nature protection. Train and fine-tune a deep learning model to classify satellite images into 10 LULC categories.

Sentinel-2 Satellite Images Semantic Segmentation with U-NET

Detection of solar panels from satellite images using deep learning

Prediction of rainfall which varies both spatially and temporally is extremely challenging. Infrared and visible spectral data from satellites have been extensively used for rainfall prediction. In this study, two deep learning methods MLP and LSTM are discussed at length for predicting precipitation at a fine spatial (10km × 10km) and temporal (hourly) resolution for the state of Gujarat. These methods are applied by using the multispectral (VIS, SWIR, MIR, WV, TIR1, TIR2) channel data such as cloud top temperature and radiance values of the INSAT-3D satellite (ISRO) as features for the model. Textural features of satellite images are incorporated by considering mean and standard deviation of each pixel’s neighbourhood. Rainfall also heavily depends on the elevation and vegetation of earth’s surface so we have used SRTM DEM and AWIFS NDVI respectively. Measurements of actual rainfall are obtained from AWS (point source stations) and TRMM (10km × 10km resolution). First dataset contains only TIR1 band temperature and AWS rainfall data for training but the second dataset includes multispectral channel data and TRMM rainfall data which brought about great improvement in results. For each data- set, a comparison between MLP and LSTM models is discussed here. We were able to classify the rainfall into nil (0mm), low ( < 2mm), medium ( > = 2mm and < 5mm) and high ( > = 5 mm) with a high accuracy. Metrics like accuracy, precision, recall and fscore have been computed to get better insights about the dataset and its corresponding outcome. Our results show that LSTM performs significantly better than MLP for any given balanced class data-sets.

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Detection of burned areas using deep learning from satellite images

Implementation of "Multi-task Deep Learning for Satellite Image Pansharpening and Segmentation"

Estimate vegetation height from satellite images using deep learning.

Super-Resolution of Satellite Images using Deep Learning