scholarly journals GEOMETRIC OBJECT BASED BUILDING RECONSTRUCTION FROM SATELLITE IMAGERY DERIVED POINT CLOUDS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 73-78
Author(s):  
Z. Li ◽  
B. Xu ◽  
J. Shan
Keyword(s):  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Images ◽  
Point Clouds ◽  
Geometric Object ◽  
Aerial Images ◽  
Reconstruction Method ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Building Reconstruction

<p><strong>Abstract.</strong> 3D building models are needed for urban planning and smart city. These models can be generated from stereo aerial images, satellite images or LiDAR point clouds. In this paper, we propose a geometric object-based building reconstruction method from satellite imagery derived point clouds. The goal is to achieve a geometrically correct, topologically consistent, and non-redundant 3D representation for buildings in urban areas. The paper first introduces our motivation, followed by a comprehensive review on related works. We then introduce the methodology and process developed in this paper. Primary results from the point clouds generated from WorldView high resolution satellite images are used to demonstrate the performance of the approach.</p>

scholarly journals PHOTOGRAMMETRIC POINT CLOUDS GENERATION IN URBAN AREAS FROM INTEGRATED IMAGE MATCHING AND SEGMENTATION

2017 ◽  
Vol IV-2/W4 ◽  
pp. 279-285
Author(s):  
L. Ye ◽  
B. Wu
Keyword(s):  
High Resolution ◽  
Image Matching ◽  
Urban Areas ◽  
Laser Scanning ◽  
Tall Buildings ◽  
Point Clouds ◽  
Aerial Images ◽  
High Resolution Imagery ◽  
Dense Matching ◽  
High Resolution Satellite Images

High-resolution imagery is an attractive option for surveying and mapping applications due to the advantages of high quality imaging, short revisit time, and lower cost. Automated reliable and dense image matching is essential for photogrammetric 3D data derivation. Such matching, in urban areas, however, is extremely difficult, owing to the complexity of urban textures and severe occlusion problems on the images caused by tall buildings. Aimed at exploiting high-resolution imagery for 3D urban modelling applications, this paper presents an integrated image matching and segmentation approach for reliable dense matching of high-resolution imagery in urban areas. The approach is based on the framework of our existing self-adaptive triangulation constrained image matching (SATM), but incorporates three novel aspects to tackle the image matching difficulties in urban areas: 1) occlusion filtering based on image segmentation, 2) segment-adaptive similarity correlation to reduce the similarity ambiguity, 3) improved dense matching propagation to provide more reliable matches in urban areas. Experimental analyses were conducted using aerial images of Vaihingen, Germany and high-resolution satellite images in Hong Kong. The photogrammetric point clouds were generated, from which digital surface models (DSMs) were derived. They were compared with the corresponding airborne laser scanning data and the DSMs generated from the Semi-Global matching (SGM) method. The experimental results show that the proposed approach is able to produce dense and reliable matches comparable to SGM in flat areas, while for densely built-up areas, the proposed method performs better than SGM. The proposed method offers an alternative solution for 3D surface reconstruction in urban areas.

scholarly journals Urban areas analysis using satellite image segmentation and deep neural network

2019 ◽  
Vol 135 ◽  
pp. 01064
Author(s):  
Vladimir Khryaschev ◽  
Leonid Ivanovsky
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Urban Areas ◽  
High Performance ◽  
Satellite Images ◽  
Satellite Image ◽  
Aerial Images ◽  
Building Detection ◽  
High Resolution Satellite Images

The goal of our research was to develop methods based on convolutional neural networks for automatically extracting the locations of buildings from high-resolution aerial images. To analyze the quality of developed deep learning algorithms, there was used Sorensen-Dice coefficient of similarity which compares results of algorithms with real masks. These masks were generated automatically from json files and sliced on smaller parts together with respective aerial photos before the training of developed convolutional neural networks. This approach allows us to cope with the problem of segmentation for high-resolution satellite images. All in all we show how deep neural networks implemented and launched on modern GPUs of high-performance supercomputer NVIDIA DGX-1 can be used to efficiently learn and detect needed objects. The problem of building detection on satellite images can be put into practice for urban planning, building control of some municipal objects, search of the best locations for future outlets etc.

scholarly journals Vehicle Detection and Classification from High Resolution Satellite Images

2014 ◽  
Vol II-1 ◽  
pp. 1-8 ◽  
Author(s):  
L. Abraham ◽  
M. Sasikumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Vehicle Detection ◽  
Satellite Imaging ◽  
Suitable Alternative ◽  
High Resolution Satellite Images ◽  
Technological Developments ◽  
High Resolution Satellite Imagery

In the past decades satellite imagery has been used successfully for weather forecasting, geographical and geological applications. Low resolution satellite images are sufficient for these sorts of applications. But the technological developments in the field of satellite imaging provide high resolution sensors which expands its field of application. Thus the High Resolution Satellite Imagery (HRSI) proved to be a suitable alternative to aerial photogrammetric data to provide a new data source for object detection. Since the traffic rates in developing countries are enormously increasing, vehicle detection from satellite data will be a better choice for automating such systems. In this work, a novel technique for vehicle detection from the images obtained from high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, difficult to distinguish from the background. But we are able to obtain a detection rate not less than 0.9. Thereafter we classify the detected vehicles into cars and trucks and find the count of them.

scholarly journals Geoinformatics Based Sprawl Dynamics Analysis of Berhampur Development Authority Area, Odisha, India

2019 ◽  
Vol 8 (4) ◽  
pp. 10471-10477
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Spatial Information ◽  
Spatial Dimension ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Authority Area ◽  
Arc Gis ◽  
Spatio Temporal ◽  
Gis Software

Urban and Regional planners need accurate and authentic spatio-temporal information of urban sprawls for efficient and sustainable planning of towns & cities worldwide. Geoinformatics powered with temporal high resolution satellite images, Geographic Information System (GIS), mobile technology, etc is now emerged as the most powerful tool for mapping and monitoring the sprawls of urban habitations. In this paper an attempt is made for analysing the dynamics of sprawls of three statutory towns of Berhampur Development Authority (BeDA) area of Ganjam District, Odisha state, India. The spatial information of urban sprawl of each town has been generated using openly available toposheets and multi -sensor & multi - temporal satellite images and the spatio temporal characteristics of sprawls has been analysed in Arc GIS software. The sprawl area as well as the population of the three towns have been analysed and the future scenario of sprawl-population dynamics has been forecasted for the years 2021 and 2031.The result of this paper highlights that sprawls of the three towns i.e Berhampur, Chhatrapur and Gopalpur will expand their spatial dimension by 22,18 and 97 percent by 2031 whereas population of the three towns will increase by 43, 19 and 15 percent between 2011 -2031.Finally the result indicates that there will be decrease in population density in the three towns which will ultimately force the Development Authority to plan more basic infrastructures and transportation in the newly expanded urban areas.

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