Urban land use extraction from very high resolution remote sensing images

Li, Mengmeng

doi:10.3990/1.9789036543286

Cited by 3 publications

(3 citation statements)

References 124 publications

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“…The use of deep learning to identify general building footprint has been very common [34], but few segmentation experiments have been conducted using neural networks for the specific building type of prefabricated building. Li et al [35] use the traditional decision tree model to extract the prefabricated building. Yang et al [36] constructed the model based on the spectral, texture, and geometric features of prefabricated buildings.…”

Section: Semantic Segmentation Of Prefabricated Buildingmentioning

confidence: 99%

Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

Wang,

Xiong,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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The development of deep learning techniques such as convolutional neural networks (CNN) has injected new vitality into the study of semantic segmentation. Benefiting from the development of new technology, the automatic and intelligent work of remote sensing image segmentation has also entered a new stage. With the further improvement of China’s urbanization rate, urban construction is also developing rapidly. One of the most important tasks in land surveillance is to monitor buildings above ground using remote sensing images. Prefabricated building is the most common type of illegal building in developing area. At present, supervision mainly relies on the traditional human-centered method, which is time-consuming and low in efficiency. Whether the deep learning methods can benefit the prefabricated building segmentation has not yet been tested. By taking the realistic demand into account, this paper proposes a customized neural network structure, which combines multiple refined modules and improves the feature extraction, especially for the prefabricated building segmentation task. Through the comparison with the baseline algorithm, the specifically refined model for the prefabricated building has shown better results in our experimental area, which proves the feasibility and superiority in practical application.

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Section: Semantic Segmentation Of Prefabricated Buildingmentioning

confidence: 99%

Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

Wang,

Xiong,

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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“…The study was able to extract boundary orientations from data with low signal-to-noise ratios. (Li, 2017) In his doctoral dissertation extracted urban land use in an object-based image analysis setting by means of analyzing urban land cover obtained from very high resolution images.…”

Section: Related Workmentioning

confidence: 99%

Object Oriented Classification and Feature Extraction for Parts of East Delhi Using Hybrid Approach

Kumar

Ravindranath²,

Raj³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Rapid urbanization of Indian cities requires a focused attention with respect to preparation of Master Plans of cities. Urban land use/land cover from very high resolution satellite data sets is an important input for the preparation of the master plans of the cities along with extraction of transportation network, infrastructure details etc. Conventional classifiers, which are pixel based do not yield reasonably accurate urban land use/land cover classification of very high resolution satellite data (usually merged images of Panchromatic &amp; Multispectral). Object Based Image Classification techniques are being used to generate urban land use maps with ease which is GIS compatible while using very high resolution satellite data sets. In this study, Object Based Image Analysis (OBIA) has been used to create broad level urban Land Use / Land Cover (LU/LC) map using high resolution ResourceSat-2 LISS-4 and Cartosat-1 pan-sharpened image on the study area covering parts of East Delhi City. Spectral indices, geometric parameters and statistical textural methods were used to create algorithms and rule sets for feature classification. A LU/LC map of the study area comprising of 4 major LU/LC classes with its main focus on separation of barren areas from built up areas has been attempted. The overall accuracy of the result obtained is estimated to be approximately 70%.</p>

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“…Therefore, how to extract buildings with algorithms rather than human experts is an immediate challenge to be addressed. Traditional extraction methods can generally be divided into feature detection-based methods [5][6][7], area segmentationbased methods [8][9][10][11] and auxiliary information-combined methods [12][13][14][15][16][17][18]. However, based on handcrafted features such as spectral, shadow, and texture features, these traditional methods can only process the low-or mid-level information contained in images, and their building extraction results usually have poor accuracy and integrity [19].…”

Section: Introductionmentioning

confidence: 99%

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

Qiu

Yin

et al. 2022

Remote Sensing

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There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks.

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Urban land use extraction from very high resolution remote sensing images

Cited by 3 publications

References 124 publications

Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

Prefab-Net: Segmentation of prefabricated building from satellite imagery with deep neural network

Object Oriented Classification and Feature Extraction for Parts of East Delhi Using Hybrid Approach

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

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