Can building footprint extraction from LiDAR be used productively in a topographic mapping context?

Chapter 3Light Detection and Ranging (LiDAR) is a quick and economical method for obtaining cloud-point data that can be used in various disciplines and a diversity of applications. LiDAR is a technique that is based on laser technology. The process looks at the two-way travel time of laser beams and measures the time and distance travelled between the laser sensor and the ground (Shan & Sampath, 2005). National Mapping Agencies (NMAs) have traditionally relied on manual methods, such as photogrammetric capture, to collect topographic detail. These methods are laborious, work-intensive, lengthy and hence, costly. In addition because photogrammetric capture methods are often time-consuming, by the time the capture has been carried out, the information source, that is the aerial photography, is out of date (Jenson and Cowen, 1999). Hence NMAs aspire to exploit methods of data capture that are efficient, quick, and cost-effective while producing high quality outputs, which is why the application of LiDAR within NMAs has been increasing. One application that has seen significant advances in the last decade is building footprint extraction (Shirowzhan and Lim, 2013). The buildings layer is a key reference dataset and having up-to-date, current and complete building information is of paramount importance, as can be witnessed with government agencies and the private sectors spending millions each year on aerial photography as a source for collecting building footprint information (Jenson and Cowen, 1999). In the last decade automatic extraction of building footprints from LiDAR data has improved sufficiently to be of an acceptable accuracy for urban planning (Shirowzhan and Lim, 2013).peer-reviewe

Object-Based Building Boundary Extraction from Lidar Data

Lidar is a remote sensing technology that uses laser beams to generate high-accuracy, three-dimensional (3D) information of the Earth. As urban areas are developing and expanding rapidly, lidar applications such as 3D building modelling and city mapping are of increasing importance. Hence building boundary extraction is one of the main applications of lidar in civil engineering and urban planning projects. In this paper, three boundary extraction algorithms including an alpha-shape algorithm, a modified concave hull algorithm and a grid-based algorithm are tested to assess their object-by-object accuracy. The alpha-shape algorithm generates reliable boundaries for most of sample buildings, while the grid-based algorithm shows less consistency in some cases. The concave hull algorithm performs moderately with a few limitations. Advantages and disadvantages of each algorithm are identified and addressed in this paper

Building modeling from airborne laser scanning point clouds of low density

A Laser scanning is a relatively recent remote sensing method which nevertheless quickly gained a prominent position, especially in the area of building detection and 3D modeling. Methods for building detection and 3D modeling initially used model-driven approaches which compare a laser scanning point cloud to a set of predefined building models. A method for determining building roof types using such approaches was presented in the article of Hofman, Potůčková (2012). An important advantage of model-driven approaches is their relative robustness to various data deficiencies such as low point density or low spatial accuracy. However, output of such methods is limited to a predefined set of building models and does not allow for diversity of actual buildings. For this reason, approaches used almost exclusively nowadays are data-driven. These methods search in datasets for a set of primitives (mostly roof planes) that are subsequently used to form the final model. This approach benefits from universality of resulting models but requires generally high data quality, especially in respect to input point cloud densities. The study of Hofman, Potůčková (2017) presented a data-driven method that can reliably detect buildings even in a very sparse point cloud in spite of using data-driven approach. At a density of...A Laserové skenování je relativně mladá metoda dálkového průzkumu Země, která si ale rychle získala významné postavení zejména v oblasti detekce a modelování budov a dalších výškových objektů. Metody pro detekování a 3D modelování budov zpočátku využívaly zejména přístupů "řízených modelem" (model-driven), které porovnávají rozložení mračna laserových bodů se sadou předdefinovaných modelů. Metoda určující typ střešního pláště pomocí takového přístupu byla představena v článku Hofman, Potůčková (2012). Velikou výhodou přístupu řízeného modelem je relativní odolnost vůči nedostatkům dat, zejména nízké hustotě bodového mračna, polohové nepřesnosti bodů atd. Naopak nedostatkem těchto metod je omezení výstupu na přednastavenou sadu modelů, která nemůže obsáhnout rozmanitost reálných budov. Z tohoto důvodu se v současnosti téměř výhradně používá přístupů "řízených daty" (data-driven). Tyto metody hledají v datech pouze sadu primitiv, nejčastěji střešních rovin, ze kterých se výsledný model dodatečně skládá. Zásadním přínosem je mnohem vyšší univerzálnost výsledných modelů. Naopak nevýhodou jsou obecně vyšší nároky na kvalitu dat, zejména hustotu bodového mračna. Ve studii Hofman, Potůčková (2017) byla představena metoda, která ačkoliv využívá přístupu řízeného daty, dokáže spolehlivě detekovat budovy i ve velmi...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Urban Feature Classification from Remote Sensor Imagery Using Deep Neural Networks

Convolutional neural networks have been shown to have a very high accuracy when applied to certain visual tasks and in particular semantic segmentation. In this thesis we address the problem of semantic segmentation of buildings from remote sensor imagery. We explore different architectures to semantic segmentation and propose ICT-Net: a novel network with the underlying architecture of a fully convolutional network, infused with feature re-calibrated Dense blocks at each layer. Uniquely, the proposed network (ICT-Net) combines the localization accuracy and use of context of the U-Net network architecture, the compact internal representations and reduced feature redundancy of the Dense blocks, and the dynamic channel-wise feature re-weighting of the Squeeze-and-Excitation(SE) blocks. The proposed network has been tested on two benchmark datasets and is shown to outperform all other state-of-the-art by more than 1.5% on the Jaccard index on INRIA’s dataset and 1.8% on the Jaccard index on AIRS dataset. Furthermore, as the building classification is typically the first step of the reconstruction process, in the latter part of the work we investigate the relationship of the classification accuracy to the reconstruction accuracy. A comparative quantitative analysis of reconstruction accuracies corresponding to different classification accuracies confirms the strong correlation between the two. We present the results which show a consistent and considerable reduction in the reconstruction accuracy. The work presented in this thesis has been published in the 16th Conference on Computer and Robot Vision 2019

Quantitative Comparison of Lidar Data and User-generated Three-dimensional Building Models From Google Building Maker

Volunteered geographic information (VGI) has received increased attention as a new paradigm for geographic information production, while light detection and ranging (LiDAR) data is widely applied to many fields. This study quantitatively compares LiDAR data and user-generated 3D building models created using Google Building Maker, and investigate the potential applications of the quantitative measures in support of rapid disaster damage assessment. User-generated 3D building models from Google Building Maker are compared with LiDAR-derived building models using 3D shape signatures. Eighteen 3D building models are created in Fremont, California using the Google Building Maker, and six shape functions (distance, angle, area, volume, slope, and aspect) are applied to the 18 LiDAR-derived building models and user-generated ones. A special case regarding the comparison between LiDAR data and building models with indented walls is also discussed. Based on the results, several conclusions are drawn, and limitations that require further study are also discussed

Spatiotemporal Modelling of Rooftop Rainwater Harvesting with LiDAR Data in the Taita Hills, Kenya

The puzzling thing about water is that, while it is very abundant in our planet – earth, millions of people globally face water scarcity. Some places, however, do not. In other places, it is not really that there is no water at all, but it is not available all year round, in most cases. This underscores the importance of putting into cognizance, the spatial and temporal context of water scarcity, hence, the basis for this project. Developing countries, especially have had worse situations with water scarcity due to population explosion and the lack of the technological advancement to harness, purify, transport, store, deliver and reuse water. One of such countries is Kenya, where many do not have access to potable water. Many solutions have been proffered without adequately addressing the issue itself. Rooftop rainwater harvesting is a potential solution to ameliorate this problem. In this thesis, I took a holistic approach to evaluate the potential of Rooftop Rainwater Harvesting (RRWH) in meeting the domestic water needs of the Taita People, Kenya. Importantly, contrary to other RRWH studies, I attempt to introduce and synergize the temporal aspect with the spatial context, in order to deeply understand the monthly dynamics of RRWH. This is crucial in answering the ‘where’ and ‘when’ questions of RRWH. This aims to provide a decision support for stakeholders, by presenting the results visually and quantifiably. The project is mainly divided into three parts. The first part involves the validation and utilization of a Light and Range Detection (LiDAR) data, for automatically generating the footprints of roofs in Taita. Herein, I compared the accuracies of LiDAR datasets from same area but different years. The second part utilizes the roofs’ polygons generated from the LiDAR data to estimate the Rooftop Rainwater Harvesting Potential in the region, by integrating it with Climatologies at high resolution for the earth’s land surface areas (CHELSA) and a strategically chosen universal roof coefficient. Lastly, household survey was carried out in the study area to understand the social context and integrate the data into my model. The result shows that there is a clear temporal trend to RRWHP in the area, and a single annual RRWHP model might be too generalized to give sufficient insight into understanding how much the system can mitigate water problem in the area. It also logically incorporates the survey data into the model to provide information about measurable monthly and annual values, as to percentage of the households that RRWH can fulfill their needs

Building Footprint Extraction from LiDAR Data and Imagery Information

This study presents an automatic method for regularisation of building outlines. Initially, building segments are extracted using a new fusion method. Data- and model-driven approaches are then combined to generate approximate building polygons. The core part of the method includes a novel data-driven algorithm based on likelihood equation derived from the geometrical properties of a building. Finally, the Gauss-Helmert and Gauss-Markov models adjustment are implemented and modified for regularisation of building outlines considering orthogonality constraints