Automated Building Extraction using High Resolution Satellite Imagery though Ensemble Modelling and Machine Learning

Das, Soumya K.; Prakash, Prithvi; Aithal, Bharath H.

doi:10.21523/gcj1.18020103

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Additionally, various improved approaches have been adopted to enhance the LU/LC estimation, such as imagery spectral pan-sharpening algorithms [11,12]. To perform any image classification, several stages need to be completed, including (1) selection of training sites based on visual image interpretation techniques; (2) selection of testing samples based on fieldwork or other techniques; (3) preprocessing steps such as geometric, atmospheric, and radiometric corrections; (4) object extraction; (5) selection of a classifier approach; (6) post-classification; and (7) result validation [7,13,14].…”

mentioning

confidence: 99%

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Dibs¹,

Jaber²,

Al‐Ansari³

2023

Emerg Sci J

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Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PDF

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confidence: 99%

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Dibs¹,

Jaber²,

Al‐Ansari³

2023

Emerg Sci J

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“…The region of Al-Qasim subdistrict is about 528 Km2. The location of the Al-Qasim subdistrict in longitude and latitude of 44°41′21″E and 32°18′5″N [7]. Samples were then packed in a plastic bag and labeled with their types, sample codes and corrodent location.…”

Section: Area Of Studymentioning

confidence: 99%

Natural Radioactivity for some Types of Date in Al-Qasim Subdistrict, Babylon Governorate

Jafer,

Azeez,

Abojassim

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The aim of the present study is to assess some radiological hazard risk parameters such as the annual effective dose (AED), Fatal Cancer Risk (FCR), Hereditary Cancer Risk (HCR), radium equivalent (Raeq), and Internal hazard index (Hin) due to specific activity of 238U, 232Th, and 40K in some samples of date in Al-Qasim subdistrict, Babylon governorate. The specific activity was measured using gamma-ray spectroscopy with NaI(Tl) doctor. The average values of the specific activity in unit Bq/kg for 238U, 232Th, and 40K were 10.83 ±1.22, 4.94 ±0.80, and 341.36 ±15.25, respectively. While, the average values of total AED, FCR×10-3, HCR×10-3, Raeq, and Hin were 0.210±0.018 mSv/y, 0.737±0.064, 0.198±0.008, 44.18±3.10 Bq/kg, and 0.148±0.011, respectively. The results show that, all values of 238U, 232Th, and 40K were lower than the global limit according to UNSCEAR 2008 (33 Bq/kg, 45 Bq/kg, and 420 Bq/kg), except two samples have 40K higher than global limit. these because increasing used chemical fertilizers by peasants. Also, it is found that all values of the radiological hazard risk parameters in the present study were lower than global limit according to UNSCEAR, ICRP, OECD, etc. So, it was concluded that the date samples in the present study does not cause any danger to the human health when its consumption.

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“…The outlier in training is well handled by RF is advantageous in classifying spatial data efficiently (Horning, 2010). SVM works on the principle of identifying right hyperplanes that maximizes the distance to the closest data points on both sides of the classes (Vapnik, 1995;Dixon, 2008;Das et al, 2018). SVM is a supervised learning technique and it can be applied to a multiclass classification.…”

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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Automated Building Extraction using High Resolution Satellite Imagery though Ensemble Modelling and Machine Learning

Cited by 5 publications

References 33 publications

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Natural Radioactivity for some Types of Date in Al-Qasim Subdistrict, Babylon Governorate

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

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