Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning

TL;DR

This paper presents a deep learning approach using CNNs for semantic segmentation of vegetation in remote sensing imagery, addressing the challenge of processing large-scale satellite data efficiently.

Contribution

It introduces a multi-modal, spatio-temporal dataset from publicly available remote sensing data and evaluates CNN models for vegetation classification.

Findings

CNN models effectively distinguish different vegetation classes

The dataset demonstrates feasibility for large-scale remote sensing analysis

Deep learning improves automation in geospatial data processing

Abstract

In recent years, the geospatial industry has been developing at a steady pace. This growth implies the addition of satellite constellations that produce a copious supply of satellite imagery and other Remote Sensing data on a daily basis. Sometimes, this information, even if in some cases we are referring to publicly available data, it sits unaccounted for due to the sheer size of it. Processing such large amounts of data with the help of human labour or by using traditional automation methods is not always a viable solution from the standpoint of both time and other resources. Within the present work, we propose an approach for creating a multi-modal and spatio-temporal dataset comprised of publicly available Remote Sensing data and testing for feasibility using state of the art Machine Learning (ML) techniques. Precisely, the usage of Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation that are present in the proposed dataset. Popularity and success of similar methods in the context of Geographical Information Systems (GIS) and Computer Vision (CV) more generally indicate that methods alike should be taken in consideration and further analysed and developed.