Mapping earth mounds from space

TL;DR

This paper investigates automatic detection of earth mounds and spotted vegetation landscapes from satellite imagery using deep learning, benchmarking several models across different regions to assess their effectiveness.

Contribution

It provides a benchmark of state-of-the-art deep networks for mapping earth mounds from space, highlighting current limitations and the need for further research.

Findings

Deep learning models show promising results but are not yet fully reliable.

Different landscapes and regions pose varying challenges for automatic mapping.

More advanced methods are required for accurate large-scale earth mound detection.

Abstract

Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetation spots in semi-arid shrublands result from extreme resource depletion and prefigure catastrophic shift of the ecosystem to a homogeneous desert, while termite mounds also producing spotted landscapes were shown to increase robustness to climate change. Yet, their identification at large scale calls for automatic methods, for instance using the popular deep learning framework, able to cope with a vast amount of remote sensing data, e.g., optical satellite imagery. In this paper, we tackle this problem and benchmark some state-of-the-art deep networks on several landscapes and geographical areas. Despite the promising results we obtained, we found that more research is needed to be able to map automatically these earth mounds from space.