Food

Deep Learning in the Mapping of Agricultural Land Use Using Sentinel-2 Satellite Data

Gurwinder Singh

In this study, deep learning (U-Net) has been implemented in the mapping of different agricultural land use types over a part of Punjab, India, using the Sentinel-2 data. As a comparative analysis, a well-known machine learning random forest (RF) has been tested. To assess the agricultural land, the major winter season crop types, i.e., wheat, berseem, mustard, and other vegetation have been considered.

Specifications

Introduction

Land

Open Atlas, how it works

Find an algorithm

Contact the author

Create a partnership

Endless opportunities