Official code for "Tiny Object Detection in Aerial Images".

A custom YOLO_UNet approach for conducting semantic segmentation from Drone images for 22 distinct classes

Roof Information Dataset for CV-Based Photovoltaic Potential Assessment

Select regions of interest (ROI) from large-area aerial oblique data to create a small dataset containing the ROI.

Official code for "Learning to Extract Building Footprints from Off-Nadir Aerial Images"

Road Segmentation Using Aerial Images

Generated images from "Data Augmentation for Aerial Images" paper

This repository contains MATLAB® functions for deriving supraglacial lake bathymetry from ATM laser altimetry data products

The SODA Dataset is a computer vision dataset containing aerial imagery of small objects captured at different altitudes. The dataset contains 829 images and 6719 object annotations.

Multi-Phase Information Theory-Based Algorithm for Edge Detection

The primary objective of the project is to to develop an intelligent system that segments aerial images. In addition, exploratory data analysis, preprocessing of data, and thorough evaluation of experimental results are a few of the steps performed.

Segmentation of aerial images using two approaches: 1) texture features, 2) vegetation index

Segmentation model using UNET architecture with ResNet34 as encoder background, designed with PyTorch.

Object Detection from Aierial Images with Different Approaches

Official code for "A Normalized Gaussian Wasserstein Distance for Tiny Object Detection"

Contrast Enhancement of Aerial Images using a New Multi-Concept Algorithm