Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification

EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

In this paper, we address the challenge of land use and land cover classification using remote sensing satellite images. For this challenging task, we use the openly and freely accessible Sentinel-2 satellite images provided within the scope of the Earth observation program Copernicus. The key contributions are as follows. We present a novel dataset based on satellite images covering 13 differe...

Extreme Learning Machine for land cover classification

This paper explores the potential of extreme learning machine based supervised classification algorithm for land cover classification. In comparison to a backpropagation neural network, which requires setting of several user-defined parameters and may produce local minima, extreme learning machine require setting of one parameter and produce a unique solution. ETM+ multispectral data set (Engla...

Regional Scale Land Use/land Cover Classification Using Temporal Series of Modis Data

This paper describes a methodology for systematic land use/land cover classification on a regional scale, with emphasis on a low cost and highly automatized approach. This methodology is based on multitemporal analyses of surface reflectance data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which is located on board NASA’s Terra and Aqua satellites and features high temporal ...

Land-cover and Land-use Mapping

Modeling Land-use and Land-cover Change

Models are used in a variety of fields, including land change science, to better understand the dynamics of systems, to develop hypotheses that can be tested empirically, and to make predictions and/or evaluate scenarios for use in assessment activities. Modeling is an important component of each of the three foci outlined in the science plan of the Land-use and -cover change (LUCC) project (Tu...