PlanetScope Imagery for Extracting Building Inventory Information

Abstract

In order to prevent serious damages from a possible earthquake and to determine the possible losses, in settlements under earthquake risk, it is very important to extract building inventory information for further determination of the performance of existing buildings. As conventional methods, such as field investigations, can be time-consuming and costly on an urban scale, approaches that are able to speed up these processes and reduce the costs are required. Determining at least some of the data required to determine the seismic performance of an existing building using alternative methods instead of conventional methods will provide a significant advantage. The study aims to investigate the potential of PlanetScope satellite imagery for extracting building inventory information. Thus, the main objectives of the study are; to extract buildings using deep learning methods, and to determine the height and the construction period of the buildings. For this purpose, two 3-m PlanetScope satellite images were used over the study area located in Eskisehir, Turkey. Over thirty buildings were located in the study area. The results showed that with PlanetScope Imagery detached buildings can be detected with high accuracy using deep learning methods, their heights can be calculated, and the construction period can be determined. For future studies, the obtained information is planned to further be processed in a Geographical Information System (GIS) for building inventory and to be used for seismic vulnerability assessment studies of existing buildings.

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

“…According to that information, a damage index can be defined. In some cases, the construction year can also be a good indicator if a milestone year can be designated for the region [41].…”

“…The proposed approach uses remote sensing techniques to estimate the building heights from satellite images that can be provided anywhere on the earth, so the proposed methodology can be applied regardless of having an inventory database that includes building height information for the region. The reliability of retrieving building heights from remote sensing data using shadows has been successfully proven in various studies with high accuracy [41,48].…”

Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

“…Further information regarding the configuration of the model can be found in Table 1Table The evaluation of the proposed method was based on four metrics: Overall Accuracy (OA), precision, recall, and kappa coefficient. OA provides an overall measure of how well the model performs 8 , but it can be misleading if the dataset is imbalanced, meaning that one class is more represented than the other 29 . Precision evaluates how well the model identifies the positive samples.…”

“…In some cases, when the cadastral information is insufficient, DANE's technical team performs the manual labeling of the buildings where people may be living. In the first case, the information in the dispersed rural areas is imprecise and outdated; while, in the latter, carrying out this task demands quite a considerable additional consumption of time and human resources 8 . Therefore, the goal of this research is to introduce a methodology that exploits both Remote Sensing (RS) 9 and Deep Learning (DL) 10 techniques in order to detect buildings' locations automatically in the dispersed rural areas of the Las Piedras sub-basin.…”

“…Moreover, the use of a semantic segmentation approach with VHR images also makes it challenging to apply this methodology to map large spatial extensions in a reasonable amount of time. In this 3 research works 8,16,17 , they used PlanetScope imagery to map individual houses. In 8 , Gordana and Onur explored a high spatial resolution PlanetScope dataset for extracting houses inventory information in order to prevent serious damages and to determine the possible loses from earthquakes.…”