MBNet: Multi-Branch Network for Extraction of Rural Homesteads Based on Aerial Images

Wei, Ren; Fan, Beilei; Wang, Yuting; Ailian, Zhou; Zhao, Zijuan

doi:10.3390/rs14102443

Cited by 9 publications

(8 citation statements)

References 42 publications

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“…Concerning the same issue, our method instead offers a holistic solution for accurate rural building segmentation. Considering another problem of dense arrangement, Wei et al [38] proposed a network model with three decoding branches for extracting rural homesteads. The addition of the detailed branch and point-to-point module is particularly useful when dealing with boundaries of the closely connected rural buildings.…”

Section: Rural Building Extractionmentioning

confidence: 99%

Efficient Rural Building Segmentation via a Multilevel Decoding Network

Xu,

Dong,

Xia

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article addresses the problem of building segmentation for rural areas with high-resolution remote sensing images. Due to the irregular spatial distribution of rural buildings, it is often challenging to perform pixel-wise dense prediction to entire areas like the usual segmentation task to extract buildings. Specifically, we present a multilevel decoding network model that classifies the input image on the patch and image levels according to the distribution of buildings. A scene head module is used to identify scenes defined as patches that contain buildings. Depending on the scene classification results, a decode gate is taken to determine the level of prediction. This hierarchical extraction strategy reduces the amount of inference time. Experiments on our constructed rural building dataset with large-scale images validate the high efficiency of the proposed method.

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Section: Rural Building Extractionmentioning

confidence: 99%

Efficient Rural Building Segmentation via a Multilevel Decoding Network

Xu,

Dong,

Xia

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In further research, we will fully consider the use of the population density data for validation analysis and combine it with the regional population policy to obtain more rigorous research results. At the same time, we will continue to explore how to efficiently and quickly obtain historical rural settlement data [60] to obtain more refined rural settlement data, aiming to make the simulation results of rural land use structure more realistic and better simulate the development process of LUCC at a micro-scale.…”

Section: Future Enhancement Of This Researchmentioning

confidence: 99%

Simulating the Coupling of Rural Settlement Expansion and Population Growth in Deqing, Zhejiang Province, Based on MCCA Modeling

Zhao

Fan

Zhou

et al. 2022

Land

Self Cite

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Analyzing the relationship between rural settlements and rural population change under different policy scenarios is key in the sustainable development of China’s urban and rural areas. We proposed a framework that comprised the mixed land use structure simulation (MCCA) model and the human–land coupling development model to assess the spatiotemporal dynamic changes in rural settlements and its’ coupling relationship with the rural population in the economically developed region of Deqing, Zhejiang Province. The results showed that rural settlements and urban land increased by 14.36 and 29.07 km2, respectively, over the last 20 years. The expansion of some rural settlements and urban land occurred at the cost of cropland occupation. Rural settlements showed an expansion trend from 2000 to 2020, increasing from 42.69 km2 in 2000 to 57.05 km2 in 2020. In 2035, under the natural development scenario, the cropland protection scenario, and the rural development scenario, rural settlements are projected to show an expansion trend and Wukang and Leidian are the key regions with rural settlement expansion. The distance to Hangzhou, nighttime light data, distance to rivers, and precipitation are important factors influencing the expansion of rural settlements. The coupling relationship between rural settlements and the rural population developed in a coordinated manner from 2000 to 2020. For 2035, under different scenarios, the coupling relationship between rural settlements and the rural population showed different trends. In the rural development scenario, the highest number of towns with coordinated development between rural settlements and the rural population is in Deqing, predominantly with Type I coupling. Overall, an important recommendation from this study is that the sustainable development of regional land use can be promoted by controlling the occupation of cropland for urban and rural construction, balancing rural settlement expansion and rural population growth, and formulating land use policies that are more suitable for rural development.

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“…Furthermore, the use of complex techniques such as semantic segmentation makes large-scale mapping impracticable. In summary, after conducting a literature review [19][20][21][22][23] , we could not find any research that has developed a methodology capable of mapping houses at an individual level on a large scale and in dispersed rural areas using PlanetScope imagery. Therefore, this research aims at addressing this gap in literature.…”

Section: Introductionmentioning

confidence: 95%

Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

Arrechea-Castillo,

Solano-Correa,

Muñoz-Ordóñez

et al. 2023

Geospatial Informatics XIII

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The Sustainable Development Goal (SDG) number 11 aims at making cities and human settlements more inclusive, safe, resilient, and sustainable. Complying with SDG 11 is a difficult task, especially when considering rural settlements where: (i) population settles in a dispersed manner; and (ii) geography complexity and social dynamics of the area make it difficult to monitor and capture data. One example of such areas can be found in the South-West of Colombia, in the Las Piedras River sub-basin. The National Administrative Department of Statistics in Colombia (DANE in Spanish) aims at mapping the population and houses in dispersed and difficult-to-access rural settlements in an accurate and continuous way. Nevertheless, there are several difficulties (derived from the in-situ way of collecting the data) that prevent such data from being generated. This research presents a methodology to carry out an updated mapping of rural areas with high spatial resolution data coming from PlanetScope (3 m). Such a mapping considers the dynamics of housing growth, focusing on dispersed and difficult-to-access rural settlements. To this aim, Convolutional Neural Networks (CNNs) are used together with PlanetScope data, allowing to account for average houses size (≥12 𝑚2) in the study area. Preliminary results show a detection accuracy above 95%, in average, according to geography complexity.

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MBNet: Multi-Branch Network for Extraction of Rural Homesteads Based on Aerial Images

Cited by 9 publications

References 42 publications

Efficient Rural Building Segmentation via a Multilevel Decoding Network

Efficient Rural Building Segmentation via a Multilevel Decoding Network

Simulating the Coupling of Rural Settlement Expansion and Population Growth in Deqing, Zhejiang Province, Based on MCCA Modeling

Mapping dispersed houses in rural areas of Colombia by exploiting planet satellite images with convolutional neural networks

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