Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts
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 t...
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Online Access: | https://www.mdpi.com/2072-4292/13/13/2618 |
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doaj-8d0484831b464182ac667c03eac4f1ea2021-07-15T15:44:41ZengMDPI AGRemote Sensing2072-42922021-07-01132618261810.3390/rs13132618Identification of Construction Areas from VHR-Satellite Images for Macroeconomic ForecastsCarsten Juergens0M. Fabian Meyer-Heß1Geomatics Group, Geography Department, Ruhr University Bochum, D-44801 Bochum, GermanyGeomatics Group, Geography Department, Ruhr University Bochum, D-44801 Bochum, GermanyThis 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.https://www.mdpi.com/2072-4292/13/13/2618urban remote sensingWorldViewconstruction areasmacroeconomic forecaststime series analysischange detection |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carsten Juergens M. Fabian Meyer-Heß |
spellingShingle |
Carsten Juergens M. Fabian Meyer-Heß Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts Remote Sensing urban remote sensing WorldView construction areas macroeconomic forecasts time series analysis change detection |
author_facet |
Carsten Juergens M. Fabian Meyer-Heß |
author_sort |
Carsten Juergens |
title |
Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts |
title_short |
Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts |
title_full |
Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts |
title_fullStr |
Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts |
title_full_unstemmed |
Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts |
title_sort |
identification of construction areas from vhr-satellite images for macroeconomic forecasts |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2021-07-01 |
description |
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. |
topic |
urban remote sensing WorldView construction areas macroeconomic forecasts time series analysis change detection |
url |
https://www.mdpi.com/2072-4292/13/13/2618 |
work_keys_str_mv |
AT carstenjuergens identificationofconstructionareasfromvhrsatelliteimagesformacroeconomicforecasts AT mfabianmeyerheß identificationofconstructionareasfromvhrsatelliteimagesformacroeconomicforecasts |
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