A revision of the land use classification system as presented in U.S. Geological Survey Circular 671

Abstract. It was introduced in this paper the Optimum-Path Forest for land use classification aiming a better environmental management, using images obtained from CBERS 2B CCD satellite covering the area of the Rio

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the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year’s trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use

Data mining techniques are studied to discover knowledge from GIS database and remote sensing image data in order to improve land use classification. Two learning granularities are proposed for inductive learning from spatial data, one is spatial object granularity, the other is pixel granularity

This paper studies the applicability of ERS-1 and JERS SAR-images to land-use classification. Different factors affecting classification accuracy like environmental conditions of study area, speckle filtering, spatial averaging, texture measures and different ways to use temporal images

and supplies water to the metropolitan area. Remote sensing techniques can be used to assess several water quality parameters and also for land use classifications. For this work the ERDAS Imagine V8.5 computer software will be used to develop a land use classification using IKONOS images. The generated land

Abstract not found

Support Vector Machine (SVM), as a powerful statistical learning method, , has been found that its performance on land-use-classification outperform conventional classifiers using multiple features extracted from lidar data and imagery. Therefore, in this paper, we use SVM for urban land-use

In this letter we present a fully automatic and computationally efficient algorithm based on the Minimum Description Length Principle (MDL) for optimizing multilayer perceptron classifiers. We demonstrate our method on the problem of multispectral Landsat image classification. We compare our