A shadow–overlapping algorithm for estimating building heights from VHR satellite images

Building height is a key geometric attribute for generating 3D building models. We propose a novel four-stage approach for automated estimation of building heights from their shadows in very-high-resolution (VHR) multispectral images. First, a building’s actual shadow regions are detected by applying ratio-band algorithm to the VHR image. Second, 2D building footprint geometries are identified using graph theory and morphological fuzzy processing techniques. Third, artificial shadow regions are simulated using the identified building footprint and solar information in the image metadata at pre-defined height increments. Finally, the difference between the actual and simulated shadow regions at every height increment is computed using Jaccard similarity coefficient. The estimated building height corresponds to the height of the simulated shadow region that resulted in the maximum value for Jaccard index. The algorithm is tested on seven urban sites in Cardiff, UK with various levels of morphological complexity. Our method outperforms the past attempts, and mean error is reduced by at least 21%

Developing a three-dimensional city modeling with the absence of elevation data

The past few decades have witnessed steady innovations in remote sensing technologies; however, elevation data needed for creating 3D city models are not reachable for several regions in all over the world. Many developed states still without proper nationwide elevation measurements dataset for developing sufficient 3D city models. The current paper addresses the possibility of producing 3D models for areas without elevation data but with footprints, measurements collected from government departments and volunteered individuals. The study aims to investigate and evaluate a different approach to create three-dimensional city models based on data that existed in open-source maps when elevation measurements are not available. The proposed approach can be divided into two stages: footprint and shadow data collection, and height estimation. At first, the footprint information and shadow area are manually gathered from satellite images, then the building height is predicted based on rooftop and shadow data. SketchUp, a 3D design software, is employed as an efficient tool for creating the 3D virtual city model. To develop such a model, the software utilizes procedural modeling in addition to an image-based approach. The developed model can produce a satisfactory and realistic virtual scene within a short time and for a large area. The 3D city modeling resulted from estimated heights is considered as a rational provisional solution at areas where elevation data are not available or are out-dated

Automatic extraction of urban structures based on shadow information from satellite imagery

The geometric visualisation of the buildings as the 3D solid structures can provide a comprehensive vision in terms of the assessment and simulation of solar exposed surfaces, which includes rooftops and facades. However, the main issue in the simulation a genuine data source that presents the real characteristics of buildings. This research aims to extract the 3D model as the solid boxes of urban structures automatically from Quickbird satellite image with 0.6 m GSD for assessing the solar energy potential. The results illustrate that the 3D model of building presents spatial visualisation of solar radiation for the entire building surface in a different direction

Automatic extraction of urban structures based on shadow information from satellite imagery

The geometric visualisation of the buildings as the 3D solid structures can provide a comprehensive vision in terms of the assessment and simulation of solar exposed surfaces, which includes rooftops and facades. However, the main issue in the simulation a genuine data source that presents the real characteristics of buildings. This research aims to extract the 3D model as the solid boxes of urban structures automatically from Quickbird satellite image with 0.6 m GSD for assessing the solar energy potential. The results illustrate that the 3D model of building presents spatial visualisation of solar radiation for the entire building surface in a different direction

Shadow detection from very high resoluton satellite image using grabcut segmentation and ratio-band algorithms

Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects

Free-Shape Polygonal Object Localization

Polygonal objects are prevalent in man-made scenes. Early approaches to detecting them relied mainly on geometry while subsequent ones also incorporated appearance-based cues. It has recently been shown that this could be done fast by searching for cycles in graphs of line-fragments, provided that the cycle scoring function can be expressed as additive terms attached to individual fragments. In this paper, we propose an approach that eliminates this restriction. Given a weighted line-fragment graph, we use its cyclomatic number to partition the graph into managebly-sized sub-graphs that preserve nodes and edges with a high weight and are most likely to contain object contours. Object contours are then detected as maximally scoring elementary circuits enumerated in each sub-graph. Our approach can be used with any cycle scoring function and multiple candidates that share line fragments can be found. This is unlike in other approaches that rely on a greedy approach to finding candidates. We demonstrate that our approach significantly outperforms the state-of-the-art for the detection of building rooftops in aerial images and polygonal object categories from ImageNet

An Approach Of Automatic Reconstruction Of Building Models For Virtual Cities From Open Resources

Along with the ever-increasing popularity of virtual reality technology in recent years, 3D city models have been used in different applications, such as urban planning, disaster management, tourism, entertainment, and video games. Currently, those models are mainly reconstructed from access-restricted data sources such as LiDAR point clouds, airborne images, satellite images, and UAV (uncrewed air vehicle) images with a focus on structural illustration of buildings’ contours and layouts. To help make 3D models closer to their real-life counterparts, this thesis research proposes a new approach for the automatic reconstruction of building models from open resources. In this approach, first, building shapes are reconstructed by using the structural and geographic information retrievable from the open repository of OpenStreetMap (OSM). Later, images available from the street view of Google maps are used to extract information of the exterior appearance of buildings for texture mapping onto their boundaries. The constructed 3D environment is used as prior knowledge for the navigation purposes in a self-driving car. The static objects from the 3D model are compared with the real-time images of static objects to reduce the computation time by eliminating them from the detection proces