Satellite Image Classification using semantic segmentation methods in deep learning

Satellite images classification

This repository will guide you how to use deep learning algorithms for land use land cover classification using satellite dataset!

Satellite Image Analytics and Earth Data Science Experiments in Python

Classifying SAT-6 data using a CNN

Few-shot satellite image classification for bringing deep learning on board OPS-SAT

pixel-wise image classification of the satellite images obtained from copernicus

This repository provides the training codes to classify aerial images using a custom-built model (transfer learning with InceptionResNetV2 as the backbone) and explainers to explain the predictions with LIME and GradCAM on an interface that lets you upload or paste images for classification and see visual explanations.

Syria's civil war destruction dataset. It can be use for anomaly detection or semantic segmentation, as well as image classification.

The application of various CNNs on an Aid dataset for satellite image classification.

Satellite image classification using a custom Convolutional Neural Network (CNN), which achieves 96% accuracy on test data . The model is designed to classify images from the EuroSAT dataset into ten distinct classes.

This repository contains code for building a deep learning model to classify satellite images into different categories such as Cloudy, Desert, Green Area, and Water.

2nd place solution for AI4EO MapYourCity Challenge: https://platform.ai4eo.eu/map-your-city

Satellite Image Classification

Application of KMeans algorithm on a satellite image and identifying its feasibility in land use classification

Satellite Image Classification Using EfficientNetB3

Train AI models on satellite image dataset to classify different types of land.

DL model deployment using docker, API deployment with FastAPI, and MLOps using WandB for overhead-mnist dataset

Residual Network implementation for classifying satellite images from EuroSAT dataset