Aggregation and visualization of spatial data with application to classification of land use and land cover

TL;DR

This paper introduces PlaniSphere, a framework that aggregates diverse geospatial data types to improve land use and land cover classification through an innovative algorithm combining remote sensing images and census data.

Contribution

The paper presents a novel framework and algorithm for integrating various geospatial datasets, enhancing the accuracy of land use and land cover classification.

Findings

Framework successfully classifies LULC from multiple data formats.

Census data can calibrate remote sensing classifications.

Approach improves environmental spatial analysis.

Abstract

Aggregation and visualization of geographical data are an important part of environmental data mining, environmental modelling, and agricultural management. However, it is difficult to aggregate geospatial data of the various formats, such as maps, census and survey data. This paper presents a framework named PlaniSphere, which can aggregate the various geospatial datasets, and synthesizes raw data. We developed an algorithm in PlaniSphere to aggregate remote sensing images with census data for classification and visualization of land use and land cover (LULC). The results show that the framework is able to classify geospatial data sets of LULC from multiple formats. National census data sets can be used for calibration of remote sensing LULC classifications. This provides a new approach for the classification of remote sensing data. This approach proposed in this paper should be useful for LULC classification in environmental spatial analysis.