Urban building extraction using satellite imagery through Machine Learning

Prakash, Prithvi; Soumya, K. D; Hariharan, Bharath

doi:10.1109/ssci.2018.8628919

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…In SVM classification, four kernel functions could be used based on the transformation needed, namely sigmoid, radial basis function, linear and polynomial. Previous studies showed that radial basis function performs best for binary classification from satellite images (Prakash et al, 2018).…”

Section: Urban Building Structure Extractionmentioning

confidence: 99%

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

Chanden¹,

Aadithyaa²,

Prakash³

et al. 2019

Proceedings of the 55th ISOCARP World Planning Congress

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Rapidly increasing population and migration from rural areas to nearby urban agglomerations develop tremendous pressure on system of the existing cities without compromising socioeconomic and cultural linkages. Policy interventions, both at global and local scale, have created newer avenues for the researchers to explore real-time solutions for problems world-wide. For instance, the outcome of 2015 United Nations agenda for the achievement of the Sustainable Development Goals (SDGs) by the year 2030 primarily focuses on urbanization issues and probabilistic modelling of future scenarios to obtain a robust alternative for resource utilization and further for maximizing sustainability through land use pattern analysis. This is the clear indication toward the very important role of “ever dormant” urban planning, especially in the case of a rapidly developing country such as India. Remote sensing and geo informatics along with Machine learning can provide extremely relevant information about the pattern change in cities and as input to visualize the future growth pockets. In this context, potential of cellular automata (CA) in urban modelling has been explored by various researchers across the globe. In the recent past, models have been drawing majority of the attention along with geographic CA processes about urban growth and urban sprawl studies. Most recent approaches include optimization of transition rules based on machine learning techniques and evolutionary algorithms that follow nature-inspired mechanism such as Genetic Algorithm, Ant colony optimization, Particle Swarm Optimization (PSO), simulated annealing, Grey Wolf optimizer etc. Irrespective of any modelling technique, model calibration remains one of the challenging and most crucial steps towards obtaining realistic results. This research communication tries to demonstrate a novel idea of integrating PSO with SLEUTH post calibration of the spatial-temporal footprint of urban growth from the year 1990 to 2017 for Kolkata, a historical megacity of Eastern India. Results were evaluated and validated using statistical fit measuresreveals PSO-SLEUTH performed substantially better compared to traditional Brute Force calibration method (BFM). Another significant development was in terms of computation time of optimized values from days (BFM) to hours (PSO). The study identifies Kolkata region to be sensitive to spread and road gravity coefficients during calibration procedure. Results indicate growth along the transport corridors with multiple agents fuelling the growth. Further, with the aid of high spatial resolution data, buildings were extracted to understand the growth parameters incorporating neural networks. Using the results, renewable energy aspects were explored to harness and provide a suitable local solution for energy issues in energy gobbling cities. Pattern of landscape change, development of better process of modeling and extraction of building from machine learning techniques for planning smart cities with self-sustaining energy is presented in this research work.

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Section: Urban Building Structure Extractionmentioning

confidence: 99%

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

Chanden¹,

Aadithyaa²,

Prakash³

et al. 2019

Proceedings of the 55th ISOCARP World Planning Congress

View full text Add to dashboard Cite

show abstract

“…Moreover, conventional road extraction from remotely sensed imagery could be made more efficient and practical; present methods do not meet the demands for real-time processing [3][4]. Traditional methods are based on pixel-level information such as support vector machine, random forest, and maximum likelihood; because they are limited to the subject of colour phenomena, these methods use only the spectral information of images [5][6]. These methods use colour reflectance to classify images, which leads to a loss of information with regions of similar colour and backgrounds [7].…”

Section: Introductionmentioning

confidence: 99%

Road segmentation: exploiting the efficiency of skip connections for efficient semantic segmentation

Madhumita

Hariharan

Devendra

et al. 2023

J. S. Asian Logistics Transport

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Extraction of features is a unique approach that has several real-time applications. In remote sensing, road feature extraction includes recognising and extracting essential information about roads from high-resolution remote sensing photos. This data may include the shape, size, direction, and connection of roadways and their surroundings we can increase the accuracy and efficiency of road recognition algorithms and better comprehend the geographical distribution of roads in metropolitan settings. Nevertheless, traditional techniques of road recognition sometimes rely on manual labour or hand-crafted characteristics, which may be costly and time-consuming. Convolutional neural networks (CNNs) have been frequently employed for road detection due to their capacity to automatically learn characteristics from data. In this research, we provide Modified LinkNet for the extraction of road features from high-resolution remote-sensing images. The proposed model incorporates several variables resulting in superior performance compared to current cutting-edge road detection techniques. SpaceNet road detection dataset yields 0.81 mIoU, which is approximately 0.1, 0.11, and 0.03 mIoU greater than DeeplabV3+, and LinkNet, respectively (base architecture). In addition, the training of the model enables more efficient inference making our method applicable to real-time applications and are therefore a more effective and efficient solution for the extraction of road features from remote sensing images.

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Automatic Extraction of Buildings from UAV-Based Imagery Using Artificial Neural Networks

Subrahmanya

Aithal

Mitra

2020

J Indian Soc Remote Sens

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Urban building extraction using satellite imagery through Machine Learning

Cited by 5 publications

References 14 publications

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

Machine learning for building extraction and integration of particle swarm optimization with sleuth for urban growth pattern visualization for liveable cities

Road segmentation: exploiting the efficiency of skip connections for efficient semantic segmentation

Automatic Extraction of Buildings from UAV-Based Imagery Using Artificial Neural Networks

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