Building Detection with Deep Learning

Kusz, Matthew; Peters, J.A.F.; Davis, Jefferson; Michael, Scott

doi:10.1145/3437359.3465573

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detecting buildings and other objects in remotely sensed images has garnered significant research interest in recent years [16][17][18][19][20][21][22], with many proposed strategies. In [23], the authors presented an adapted U-Net convolutional neural network segmentation to identify buildings using LiDAR data, based on the premise that U-Net performed well in detecting irregular edges. The study found that the model performed well for residential buildings but struggled with larger structures and had difficulty identifying diagonally oriented buildings, resulting in some buildings being merged or split.…”

Section: Related Workmentioning

confidence: 99%

Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

et al. 2023

View full text Add to dashboard Cite

Accurate building detection is a critical task in urban development and digital city mapping. However, current building detection models for high-resolution remote sensing images are still facing challenges due to complex object characteristics and similarities in appearance. To address this issue, this paper proposes a novel algorithm for building detection based on in-depth feature extraction and classification of adaptive superpixel shredding. The proposed approach consists of four main steps: image segmentation into homogeneous superpixels using a modified Simple Linear Iterative Clustering (SLIC), in-depth feature extraction using an variational auto-encoder (VAE) scale on the superpixels for training and testing data collection, identification of four classes (buildings, roads, trees, and shadows) using extracted feature data as input to an Convolutional Neural Network (CNN), and extraction of building shapes through regional growth and morphological operations. The proposed approach offers more stability in identifying buildings with unclear boundaries, eliminating the requirement for extensive prior segmentation. It has been tested on two datasets of high-resolution aerial images from the New Zealand region, demonstrating superior accuracy compared to previous works with an average F1 score of 98.83%. The proposed approach shows potential for fast and accurate urban monitoring and city planning, particularly in urban areas.

show abstract

Section: Related Workmentioning

confidence: 99%

Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

et al. 2023

View full text Add to dashboard Cite

show abstract

Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping

Huang

Jin

2022

Remote Sensing

View full text Add to dashboard Cite

Human encroachment into wildlands has resulted in a rapid increase in wildland–urban interface (WUI) expansion, exposing more buildings and population to wildfire risks. More frequent mapping of structures and WUIs at a finer spatial resolution is needed for WUI characterization and hazard assessment. However, most approaches rely on high-resolution commercial satellite data with a particular focus on urban areas. We developed a deep learning framework tailored for building footprint detection in the transitional wildland–urban areas. We leveraged meter scale aerial imageries publicly available from the National Agriculture Imagery Program (NAIP) every 2 years. Our approach integrated Mobile-UNet and generative adversarial network. The deep learning models trained over three counties in California performed well in detecting building footprints across diverse landscapes, with an F1 score of 0.62, 0.67, and 0.75 in the interface WUI, intermix WUI, and rural regions, respectively. The bi-annual mapping captured both housing expansion and wildfire-caused building damages. The 30 m WUI maps generated from these finer footprints showed more granularity than the existing census tract-based maps and captured the transition of WUI dynamics well. More frequent updates of building footprint and improved WUI mapping will improve our understanding of WUI dynamics and provide guidance for adaptive strategies on community planning and wildfire hazard reduction.

show abstract

Building Detection with Deep Learning

Cited by 2 publications

References 1 publication

Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

Building Detection in High-Resolution Remote Sensing Images by Enhancing Superpixel Segmentation and Classification Using Deep Learning Approaches

Aerial Imagery-Based Building Footprint Detection with an Integrated Deep Learning Framework: Applications for Fine Scale Wildland–Urban Interface Mapping

Contact Info

Product

Resources

About