Deep learning for land use and land cover classification from the Ecuadorian Paramo.

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...

Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps

To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prep...

Spatial simulation for translating from land use to land cover

An approach to simulating land-cover patterns based on historical land-use maps and forecasts based on models and planning documents is described and demonstrated. The approach uses stochastic spatial simulation to generate land-cover patterns on the basis of a land-use map and stated translation rules. The translation rules take the form of (1) a table that summarizes the proportions of each l...

Land-cover and Land-use Mapping