Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts

Carsten Juergens,M Fabian Meyer-Heß

doi:10.3390/rs13132618

Abstract

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.

Highlights

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Macroeconomic forecasts are well-known instruments to quantify the economic development for a country and for a certain time period
The determination of new construction areas turns out to be especially difficult, since one can hardly recognize the development of a building before its completion

Summary

Introduction

Macroeconomic forecasts are well-known instruments to quantify the economic development for a country and for a certain time period. These forecasts are based on facts or indicator variables (that indicate the macroeconomic activity) that stem from official reports and statistics, which are published in certain intervals at specific fixed dates [1]. To reduce the time lag between data analysis and the forecasting period, economists look for alternative datasets They are to provide innovative indicators that facilitate the needed forecasts in even shorter time intervals. Beyond identifying buildings in satellite imagery, it is possible to detect and precisely locate construction areas. Little cloud cover is vital for an unimpeded view of all targets

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Discussion

Conclusion