Segmentation of arbitrary features in very high resolution remote sensing imagery

TL;DR

EcoMapper is a scalable, automated deep learning framework for segmenting arbitrary features in very high resolution remote sensing imagery, enabling broad applicability across diverse geographic regions and features.

Contribution

This work introduces EcoMapper, a fully automated deep learning tool for segmenting various features in VHR remote sensing data, overcoming limitations of context-specific models.

Findings

Models trained with EcoMapper achieved competitive segmentation scores.

A relationship was identified to derive optimal ground sampling distance from feature size.

A comprehensive methodology for field surveys was developed.

Abstract

Very high resolution (VHR) mapping through remote sensing (RS) imagery presents a new opportunity to inform decision-making and sustainable practices in countless domains. Efficient processing of big VHR data requires automated tools applicable to numerous geographic regions and features. Contemporary RS studies address this challenge by employing deep learning (DL) models for specific datasets or features, which limits their applicability across contexts. The present research aims to overcome this limitation by introducing EcoMapper, a scalable solution to segment arbitrary features in VHR RS imagery. EcoMapper fully automates processing of geospatial data, DL model training, and inference. Models trained with EcoMapper successfully segmented two distinct features in a real-world UAV dataset, achieving scores competitive with prior studies which employed context-specific models. To evaluate EcoMapper, many additional models were trained on permutations of principal field survey characteristics (FSCs). A relationship was discovered allowing derivation of optimal ground sampling distance from feature size, termed Cording Index (CI). A comprehensive methodology for field surveys was developed to ensure DL methods can be applied effectively to collected data. The EcoMapper code accompanying this work is available at https://github.com/hcording/ecomapper .