Editorial for Special Issue: “Remote Sensing based Building Extraction”

Awrangjeb, Mohammad; Hu, Xiangyun; Wang, Jinhu; Tian, Jiaojiao

doi:10.3390/rs12030549

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In urban remote sensing, many approaches exist to map cities and urban areas (e.g., [2][3][4][5][6][7]), attaining high spatial precision and accuracy. For many applications, the recognition of built-up structures is of major interest (e.g., [8,9]). Besides optical aerial or satellite imagery, LiDAR images (e.g., [10][11][12]) are also used.…”

Section: Introductionmentioning

confidence: 99%

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

Jürgens

Meyer-Heß

2021

Remote Sensing

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jürgens

Meyer-Heß

2021

Remote Sensing

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“…The significance of building extraction spans a plethora of practical domains, encompassing disaster management [1], urban development analysis [2], [3], and environmental monitoring [4]. However, the path to automated building extraction from remote sensing imagery remains strewn with challenges ranging from the diversity of building appearances and sizes to the intricacies of scene complexities and incomplete cue extraction [5]. In this context, deep learning techniques have risen as a formidable answer to the challenges within the realm of computer vision tasks [6]- [9].…”

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confidence: 99%

An Attentive Hough Transform Module for Building Extraction From High Resolution Aerial Imagery

Yahia Berrouiguet,

Zigh,

Djebouri

2024

IEEE Access

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In the era of abundant high-resolution aerial imagery, the automatic extraction of buildings is indispensable for applications like disaster response, environmental monitoring, and urban growth analysis. Deep learning approaches, particularly fully convolutional networks, have exhibited remarkable performance in this challenging task. Nevertheless, the accurate identification and delineation of building boundaries pose persistent challenges hindering further improvements in building extraction precision. To tackle these, we introduce a novel deep learning architecture explicitly designed for building extraction in high-resolution aerial images. Our method addresses the precise identification of building borders by combining both local and global contextual information. We efficiently preserve object boundaries and optimize the representation of straight lines within buildings through the integration of the Attentive Hough Transform and Inverse Hough Transform (AttHT-IHT) module into the U-Net architecture. Extensive experiments on the Potsdam dataset showcase substantial enhancements in building extraction accuracy, with a 97.73 % accuracy rating and a 96.42 % recall rate. Generalization capability on the WHU satellite dataset I was assessed to validate the adaptability of our proposed method.

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Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data

Kaplan

Çömert

Kaplan

et al. 2022

IJGI

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The extraction of building inventory information is vital for damage assessment and planning and modelling studies. In the last few years, the conventional data extraction for building inventory was overcome using various remote sensing data and techniques. The main objectives of this study were to supply the necessary data for the structural engineers to calculate the seismic performance of existing structures. Thus, we investigated light detection and ranging (LiDAR) derivatives data to classify buildings and extract building inventory information, such as different heights of the buildings and footprint area. The most important data to achieve this was also investigated and classified using machine learning methods, such as Random Forest, Random Tree, and Optimized Forest, over the object-based segmentation results. All of the machine learning methods successfully classified the buildings with high accuracy, whereas the other methods outperformed RT. The height and footprint area results show that the archived sensitivity of the building inventory information is sufficient for the data to be further used in different applications, such as detailed structural health monitoring. Overall, this study presents a methodology that can accurately extract building information. In light of the results, future studies can be directed for investigations on determining the construction year using remote sensing data, such as multi-temporal satellite imagery.

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Editorial for Special Issue: “Remote Sensing based Building Extraction”

Abstract: Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]

Cited by 5 publications

References 18 publications

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

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

An Attentive Hough Transform Module for Building Extraction From High Resolution Aerial Imagery

Using Machine Learning to Extract Building Inventory Information Based on LiDAR Data

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