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

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.

Multitemporal Change Detection Analysis in an Urbanized Environment Based upon Sentinel-1 Data

“…A few buildings are not captured by the MDADT method, e.g., because they were completed before the first recording in 2015 or were still under construction during the third and last recording in 2020 (Figure 7). This pattern corresponds to the characteristic life patterns of buildings [63].…”

“…A few buildings are not captured by the MDADT method, e.g., because they were completed before the first recording in 2015 or were still under construction during the third and last recording in 2020 (Figure 7). This pattern corresponds to the characteristic life patterns of buildings [63]. In an additional time series analysis of the data set of house perimeters between 2016 and 2021, the identical building demolitions and new buildings are shown (Figure 6).…”

Remote Sensing for Short-Term Economic Forecasts

“…Here, specific change detection approaches are necessary (e.g., Radke et al, 2005 [23], Olteanu-Raimond et al, 2020 [24], Henits et al, 2016 [25]). Juergens and Meyer-Heß (2021) [26] worked with finer spatial resolution and reported on their findings related to construction areas based on mono-temporal VHR satellite images and combined change detection analysis.…”