Super-resolution-based snake model—an unsupervised method for large-scale building extraction using airborne LiDAR Data and optical image

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

COMPARISON OF ALGORITHMS FOR CONSTRUCTION DETECTION USING AIRBORNE LASER SCANNING AND NDSM CLASSIFICATION

Traditional approach to classify the point cloud of airborne laser scanning is based on the processing of a normalized digital surface model (nDSM), when ground facilities are detected and classified. The main feature to detect a ground facility is height difference between adjacent points. The simplest method to extract a ground facility is region-growing algorithm, which applies threshold to identify the connection between two points. Region growing algorithm is working with the constant value of height difference. Therefore, it is not applicable due to diverse conditions of earth surface, when height difference must be defined for each region separately. As result, researchers propose hierarchical, statistical and cluster methods to solve this problem. The study goal is to compare four algorithms to generate nDSM: region growing, progressive morphological filter, adaptive TIN surfaces and graph-cut. The experiment is divided into two stages: 1) to calculate the number of detected and lost buildings in nDSM; 2) to measure the classification accuracy of extracted shapes. The experiment results have showed that progressive morphological filter and graph-cut provides the minimal loss of buildings (only 1%). The most effective algorithm for ground facility detection is the graph-cut (total accuracy 0.95, Cohen’s Kappa 0.89, F1 score 0.93)

Machine learning for improved detection and segmentation of building boundary

The first step in rescuing and mitigating the losses from natural or man-made disasters is to assess damaged assets, including services, utilities and infrastructure, such as buildings. However, manual visual analysis of the affected buildings can be time consuming and labour intensive. Automatic detection of buildings, on the other hand, has the potential to overcome the limitations of conventional approaches. This thesis reviews the existing methods for the automated detection of objects using multi-source geospatial data and presents two novel post processing techniques. Effective building segmentation and recognition techniques are also investigated. Artificial intelligence techniques have been used to identify building boundaries in automated building-detection applications. Compared with other neural network models, the convolutional neural network (CNN) architectures based on supervised and unsupervised approaches provide better results by looking at the image details as spatial information of the entity in the frame. This research incorporates the improved semantic detection ability of Region-based Convolutional Neural Network (Mask R-CNN) and the segmentation refining capability of the conditional random field (CRF)s. Mask R-CNN uses a pre-trained network to recognise the boundary boxes around buildings. It also provides contour key points around buildings that are masked in satellite images. This thesis proposes two novel post-processing techniques that operate by modifying and detecting the building’s relative orientation properties and combining the key points predicted by the two head neural networks to modify the predicted contour with the help of the proposed novel snap algorithms. The results show significant improvements in the accuracy of boundary detection compared with the state-ofthe-art techniques of 2.5%, 4.6% and 1% for F1-Score, Intersection over Union also known as Jacard coefficient (IoU), and overall pixel accuracy, respectively. CNNs have proven to be powerful tools for a wide range of image processing tasks where they can be used to automatically learn mid-level and high-level concepts from raw data, such as images. Finally, the results highlight the potential of further approaches to these applications, such as the planning of infrastructure

Deep Learning for Building Footprint Generation from Optical Imagery

Auf Deep Learning basierende Methoden haben vielversprechende Ergebnisse für die Aufgabe der Erstellung von Gebäudegrundrissen gezeigt, aber sie haben zwei inhärente Einschränkungen. Erstens zeigen die extrahierten Gebäude verschwommene Gebäudegrenzen und Klecksformen. Zweitens sind für das Netzwerktraining massive Annotationen auf Pixelebene erforderlich. Diese Dissertation hat eine Reihe von Methoden entwickelt, um die oben genannten Probleme anzugehen. Darüber hinaus werden die entwickelten Methoden in praktische Anwendungen umgesetzt

Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System

In the last decades, Portugal has been severely affected by forest fires which have caused massive damage both environmentally and socially. Having a well-structured and precise mapping of rural roads is critical to help firefighters to mitigate these events. The traditional process of extracting rural roads centerlines from aerial images is extremely time-consuming and tedious, because the mapping operator has to manually label the road area and extract the road centerline. A frequent challenge in the process of extracting rural roads centerlines is the high amount of environmental complexity and road occlusions caused by vehicles, shadows, wild vegetation, and trees, bringing heterogeneous segments that can be further improved. This dissertation proposes an approach to automatically detect rural road segments as well as extracting the road centerlines from aerial images. The proposed method focuses on two main steps: on the first step, an architecture based on a deep learning model (DeepLabV3+) is used, to extract the road features maps and detect the rural roads. On the second step, the first stage of the process is an optimization for improving road connections, as well as cleaning white small objects from the predicted image by the neural network. Finally, a morphological approach is proposed to extract the rural road centerlines from the previously detected roads by using thinning algorithms like the Zhang-Suen and Guo-Hall methods. With the automation of these two stages, it is now possible to detect and extract road centerlines from complex rural environments automatically and faster than the traditional ways, and possibly integrating that data in a Geographical Information System (GIS), allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores, trazendo segmentos heterogéneos que podem ser melhorados. Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais, bem como extrair os eixos de via de imagens aéreas. O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+), para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização de intercessões melhorando as conexões relativas aos eixos de via, bem como a remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais como os algoritmos Zhang-Suen e Guo-Hall. Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais de grande complexidade de forma automática e com uma maior rapidez em relação aos métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo real