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

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

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.

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.

Satellite Image Classification

Satellite image classification using a custom Convolutional Neural Network (CNN). The model is designed to classify images from the EuroSAT dataset into ten distinct classes.

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

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

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