Automatic Building Footprint Extraction using Random Forest Algorithm from High Resolution Google Earth Images: A Feature-Based Approach

Thottolil, Rahisha; Kumar, Uttam

doi:10.1109/conecct55679.2022.9865829

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…Support Vector Machines (SVM) [10] and Random Forests are popular classification techniques used for categorizing individual pixels according to spectral properties. These techniques have effectively distinguished between populated and unpopulated areas [11,12]. However, with the introduction of object-based image analysis (OBIA), researchers have broadened the analysis to incorporate spatial and contextual information.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

Rawat,

Gupta,

Persello

2024

Preprint

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In our increasingly urbanized world, precise and up-to-date maps of human settlements are essential for sustainable urban development policies. The availability of open-access Sentinel-2 data from the Copernicus program presents an opportunity to create a comprehensive global map of human settlements, offering a detailed view of built areas on a large scale. This study estimates large-scale built-up fractions using encoder-decoder deep learning architectures like U-net, Res-U-net, and Attention-U-net in the large and complex urban area of Delhi, India. Openly available datasets like Open Street Map (OSM) and Microsoft building footprint datasets are used to derive built-up fractions at 10×10m resolution cells for over 34,000 km2. Our results show that Attention-U-net with the Huber loss function performs the best in different built-up densities (i.e., urban, semi-urban or rural) with an R2 score of 0.631, while Res-U-net and U-net obtained an R2 score of 0.623 and 0.612, respectively. The investigated networks significantly improve the accuracy over the latest Global Human Settlement Layer product (GHSL-S2), which uses a deep-CNN and reaches an R2 of 0.387 in our case study area. The result of this study yields a valuable spatial layer for examining the spatial distribution of human settlements across the entire spectrum from rural to urban areas.

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Section: Introductionmentioning

confidence: 99%

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

Rawat,

Gupta,

Persello

2024

Preprint

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Revolutionizing urban mapping: deep learning and data fusion strategies for accurate building footprint segmentation

Dabove,

Daud,

Olivotto

2024

Sci Rep

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In the dynamic urban landscape, understanding the distribution of buildings is paramount. Extracting and delineating building footprints from high-resolution images, captured by aerial platforms or satellites, is essential but challenging to accomplish manually, due to the abundance of high-resolution data. Automation becomes imperative, yet it introduces complexities related to handling diverse data sources and the computational demands of advanced algorithms. The innovative solution proposed in this paper addresses some intricate challenges occurring when integrating deep learning and data fusion on Earth Observed imagery. By merging RGB orthophotos with Digital Surface Models, deriving from the same aerial high-resolution surveys, an integrated consistent four-band dataset is generated. This unified approach, focused on the extraction of height information through stereoscopy utilizing a singular source, facilitates enhanced pixel-to-pixel data fusion. Employing DeepLabv3 algorithms, a state-of-the-art semantic segmentation network for multi-scale context, pixel-based segmentation on the integrated dataset was performed, excelling in capturing intricate details, particularly when enhanced by the additional height information deriving from the Digital Surface Models acquired over urban landscapes. Evaluation over a 21 km2 area in Turin, Italy, featuring diverse building frameworks, showcases how the proposed approach leads towards superior accuracy levels and building boundary refinement. Notably, the methodology discussed in the present article, significantly reduces training time compared to conventional approaches like U-Net, overcoming inherent challenges in high-resolution data automation. By establishing the effectiveness of leveraging DeepLabv3 algorithms on an integrated dataset for precise building footprint segmentation, the present contribution holds promise for applications in 3D modelling, Change detection and urban planning. An approach favouring the application of deep learning strategies on integrated high-resolution datasets can then guide decision-making processes facilitating urban management tasks.

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Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

Günen

2024

International Journal of Remote Sensing

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Automatic Building Footprint Extraction using Random Forest Algorithm from High Resolution Google Earth Images: A Feature-Based Approach

Cited by 4 publications

References 23 publications

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

Deep Learning for Built-up Fractional Mapping using Sentinel-2 images: A Case Study in Delhi, India

Revolutionizing urban mapping: deep learning and data fusion strategies for accurate building footprint segmentation

Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

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