Knowledge discovery from remote sensing images: A review

Wang, Lizhe; Yan, Jining; Mu, Lin; Huang, Liang

doi:10.1002/widm.1371

Cited by 36 publications

(12 citation statements)

References 146 publications

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“…Previous works on building rooftop extraction and recognition in high-resolution remote sensing images can be divided into two categories: artificial-feature-based methods and deep-learning-based methods [33]. The former have mainly utilized geometric, spectral, and background information about building rooftops.…”

Section: Related Work 21 Building Rooftop Recognition From Remote Sen...mentioning

confidence: 99%

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

et al. 2022

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Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Effective assessment of carbon emission reduction has become an urgent challenge for the government and for business enterprises. In this study, we propose a method to assess accurately the potential reduction of long-term carbon emission by installing solar PV on rooftops. This is achieved using the joint action of GF-2 satellite images, Point of Interest (POI) data, and meteorological data. Firstly, we introduce a building extraction method that extends the DeepLabv3+ by fusing the contextual information of building rooftops in GF-2 images through multi-sensory fields. Secondly, a ridgeline detection algorithm for rooftop classification is proposed, based on the Hough transform and Canny edge detection. POI semantic information is used to calculate the usable area under different subsidy policies. Finally, a multilayer perceptron (MLP) is constructed for long-term PV electricity generation series with regional meteorological data, and carbon emission reduction is estimated for three scenarios: the best, the general, and the worst. Experiments were conducted with GF-2 satellite images collected in Daxing District, Beijing, China in 2021. Final results showed that: (1) The building rooftop recognition method achieved overall accuracy of 95.56%; (2) The best, the general and the worst amount of annual carbon emission reductions in the study area were 7,705,100 tons, 6,031,400 tons, and 632,300 tons, respectively; (3) Multi-source data, such as POIs and climate factors play an indispensable role for long-term estimation of carbon emission reduction. The method and conclusions provide a feasible approach for quantitative assessment of carbon reduction and policy evaluation.

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Section: Related Work 21 Building Rooftop Recognition From Remote Sen...mentioning

confidence: 99%

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

et al. 2022

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“…The general scheme of the adopted methodology is shown in Figure 1. (Wang, Yan, Mu, & Huang, 2020). Due to their ability to learn high-level features, CNNs are widely used in image classification and object segmentation.…”

Section: General Objectivesmentioning

confidence: 99%

Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

et al. 2022

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An important measurable indicator of urbanization and its environmental implications has been identified as the urban impervious surface. It presents a strategy based on three-dimensional convolutional neural networks (3D CNNs) for extracting urbanization from the LiDAR datasets using deep learning technology. Various 3D CNN parameters are tested to see how they affect impervious surface extraction. For urban impervious surface delineation, this study investigates the synergistic integration of multiple remote sensing datasets of Azad Kashmir, State of Pakistan, to alleviate the restrictions imposed by single sensor data. Overall accuracy was greater than 95% and overall kappa value was greater than 90% in our suggested 3D CNN approach, which shows tremendous promise for impervious surface extraction. Because it uses multiscale convolutional processes to combine spatial and spectral information and texture and feature maps, we discovered that our proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier. In the fast-growing big data era, image analysis presents significant obstacles, yet our proposed 3D CNNs will effectively extract more urban impervious surfaces.

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“…The intelligent module based on modified approach is used to calculate vegetation indices and select the optimal number of vegetation indices using an intelligent algorithm [17]. It is a greedy approach; it makes optimum selection at each step as it tries to find the effective combination of vegetation indices.…”

Section: Algorithm For Optimum Indices Selectionmentioning

confidence: 99%

Novel approach of association rule mining for tree canopy assessment

More¹,

Nikam²,

Banerjee

2021

IJ-AI

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The evolution of technology and availability of voluminous satellite images are bringing a new scenario in satellite image classification where a performance efficient method for predictive analysis of satellite images for land cover classification needs to be devised. As urban areas are growing at faster rate, special attention needs to be given to solve tree canopy assessment problem. Vegetation indices are calculated from spectral information of satellite images. Hundreds of such vegetation indices are available to detect vegetation from a satellite image. The contribution of this paper is designing an improved Apriori algorithm to select optimal number of vegetation indices for tree canopy assessment. In this research, we propose a novel computational approach that allows the improvement of results. It selects optimal combination of vegetation indices and applies principal component analysis on it. It uses a greedy approach based on Apriori algorithm. This study emphasizes on assessment of tree canopy using GPU-enabled environment for performance-efficient assessment. The results achieved, are comparable to state-of-the-art techniques, with an accuracy of 96%. The research has considered 4 years data for Mumbai city of India. This research is useful for Green India Mission of India to assess tree canopy of urban region.

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Knowledge discovery from remote sensing images: A review

Cited by 36 publications

References 146 publications

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Accurate Recognition of Building Rooftops and Assessment of Long-Term Carbon Emission Reduction from Rooftop Solar Photovoltaic Systems Fusing GF-2 and Multi-Source Data

Urbanization Detection Using LiDAR-Based Remote Sensing Images of Azad Kashmir Using Novel 3D CNNs

Novel approach of association rule mining for tree canopy assessment

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