Urban land-use analysis using proximate sensing imagery: a survey

Qiao, Zhinan; Yuan, Xiaohui

doi:10.1080/13658816.2021.1919682

Cited by 16 publications

(3 citation statements)

References 69 publications

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“…When it comes to the data coverage, aerial imagery provides a wider coverage than SVI (Cao et al, 2018), as not all buildings may be available in SVI (see Figure 1). The street view perspective may suffer from bias when classifying small scale parcels, and this is because SVI is captured sparsely and unevenly, which may lead to inaccurate results on machine learning (Feng et al, 2018;Cao et al, 2018;Qiao and Yuan, 2021). In SVI, only scenes near streets can be captured due to the limited coverage.…”

Section: Mapping Land Usementioning

confidence: 99%

“…Features obscured by large and tall roadside structures are hidden (Cao et al, 2018). Further, the available SVI is often biased towards prosperous areas of the city (Qiao and Yuan, 2021). Qiao and Yuan (2021) also caution that imaging the streets involves the collection and sharing of proximate sensing data that can lead to privacy and trust of anonyms.…”

Section: Mapping Land Usementioning

confidence: 99%

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Comparing Street View Imagery and Aerial Perspectives in the Built Environment

Gaw

Chen

Chow

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Street view imagery (SVI) has gained prominence in the past decade, offering a new perspective to map and understand cities. It supports numerous studies in the built environment, by replacing or supplementing aerial and satellite imagery, where some studies have not yet been possible with traditional platforms and have now been enabled for the first time thanks to the increasing volume of SVI data. However, the two perspectives are often disconnected and there has not been an overarching paper to discuss the pros and cons of each. We provide an overview outlining and discussing the role of SVI in GIS and urban studies spanning six use cases. Our discourse is supported by a systematic literature review of more than 100 papers and our own experiments that reveal the added value and challenges of SVI in extracting information on buildings and other urban features, an increasingly important use case. We find that the key advantages of SVI over aerial imagery are that it represents more closely how streetscapes are perceived by people and that it enables extracting certain information that otherwise cannot be gathered from top-down perspectives. However, the spatial coverage of SVI tends to be limited to the vicinity of driveable roads, and its temporal coverage is comparatively sparse.

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Section: Mapping Land Usementioning

confidence: 99%

Section: Mapping Land Usementioning

confidence: 99%

Comparing Street View Imagery and Aerial Perspectives in the Built Environment

Gaw

Chen

Chow

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…Over the last few decades, significant progress has been made in designing efficient models for data from a single source, such as hyperspectral [3], synthetic aperture radar [4], very high-resolution images [5,6], and so forth. However, remote sensing scene classification is still regarded as a challenging task [7] when using only overhead images due to their lack of diverse detailed information.…”

Section: Introductionmentioning

confidence: 99%

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-View Images

et al. 2023

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Due to their ability to offer more comprehensive information than data from a single view, multi-view (e.g., multi-source, multi-modal, multi-perspective) data are being used more frequently in remote sensing tasks. However, as the number of views grows, the issue of data quality is becoming more apparent, limiting the potential benefits of multi-view data. Although recent deep neural network (DNN)-based models can learn the weight of data adaptively, a lack of research on explicitly quantifying the data quality of each view when fusing them renders these models inexplicable, performing unsatisfactorily and inflexibly in downstream remote sensing tasks. To fill this gap, in this paper, evidential deep learning is introduced to the task of aerial-ground dual-view remote sensing scene classification to model the credibility of each view. Specifically, the theory of evidence is used to calculate an uncertainty value which describes the decision-making risk of each view. Based on this uncertainty, a novel decision-level fusion strategy is proposed to ensure that the view with lower risk obtains more weight, making the classification more credible. On two well-known, publicly available datasets of aerial-ground dual-view remote sensing images, the proposed approach achieves state-of-the-art results, demonstrating its effectiveness.

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Prediction of mangrove recovery in natural protected areas of the Yucatan Peninsula

Osorio-Olvera,

Rioja-Nieto,

Guerra-Martínez

2024

Reg Environ Change

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Natural protected areas (NPAs) in the Yucatan Peninsula favour the conservation of mangrove forests, which are valuable ecosystems for their provision of ecosystem services. However, mangroves are vulnerable to destruction due to natural and anthropogenic pressures. Therefore, it is important to assess their spatial and temporal dynamics and the potential for deforestation and recovery of cover. In this study, we analyse and model mangrove forest cover change in six NPAs of the Yucatan Peninsula by 2025. Predictions were made using the cellular automata method (CA-Markov) based on attributes that drive rates of change (obtained Kappa coefficients between 0.78 and 0.91). Anthropogenic development was the most dominant potential driver of land use and land cover change in all NPAs except the Flora and Fauna Protection Area-Yum Balam. During the period 2005–2015, the Biosphere Reserves-Petenes and Celestún showed the greatest mangrove loss, followed by the Flora and Fauna Protection Area-Nichupté. These processes changed for the simulated period (2015–2025), where an increase in mangrove cover is projected in these protected areas. Flora and Fauna Protection Area-Términos is the only protected area where a projected transition of mangroves to anthropogenic development has been identified. Therefore, it should be considered an area vulnerable to mangrove transformation and loss.

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Urban land-use analysis using proximate sensing imagery: a survey

Cited by 16 publications

References 69 publications

Comparing Street View Imagery and Aerial Perspectives in the Built Environment

Comparing Street View Imagery and Aerial Perspectives in the Built Environment

Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-View Images

Prediction of mangrove recovery in natural protected areas of the Yucatan Peninsula

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