Geospatial assessment of urban sprawl and landslide susceptibility around the Nainital lake, Uttarakhand, India

Dey, Jaydip; Sakhre, Saurabh; Vijay, Ritesh; Bherwani, Hemant; Kumar, Rakesh

doi:10.1007/s10668-020-00731-z

Cited by 11 publications

(2 citation statements)

References 47 publications

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“…Landslides or mudslides are an extensive phenomenon, resulting in huge upheavals worldwide with a great frequency [1][2][3]. It is a significant hydro-geological threat affecting large areas of the world, and in particular the India country, including the Western Ghats, North-eastern hill areas, Himalayan regions etc.…”

Section: Introductionmentioning

confidence: 99%

A New Mask R-CNN-Based Method for Improved Landslide Detection

Ullo

Mohan

Sebastianelli

et al. 2021

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

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This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and non-landslide images. The proposed method consists of three steps: (i) augmenting training image samples to increase the volume of the training data, (ii) fine tuning with limited image samples, and (iii) performance evaluation of the algorithm in terms of precision, recall and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves Precision equals to 1.00, Recall 0.93 and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land use planners and policy makers of hilly areas where intermittent slope deformations necessitate landslide detection as prerequisite before planning.

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Section: Introductionmentioning

confidence: 99%

A New Mask R-CNN-Based Method for Improved Landslide Detection

Ullo

Mohan

Sebastianelli

et al. 2021

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

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“…Regarding the human settlement problem, our research distances itself, offering a comprehensive evaluation of deep-learning-based methods and establishing a baseline for a dataset that is ample and covers most of Mexico. In the assessment of human settlements sprawl, researchers routinely employ the change of land use alone [37], [38], combined with other descriptors [39]- [42], or an ad hoc sprawl index, sometimes the result of multivariate analysis [35], [46], [47], [70]. These assessments describe aspects that may provide information about the features considered.…”

Section: Discussionmentioning

confidence: 99%

Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning

Briseño Montes,

Salas,

Villaseñor Garcia

et al. 2024

IEEE Latin Am. Trans.

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Understanding human settlements' geographic location and extent can support decision-making in resource distribution, urban growth policies, and natural resource protection. This research presents an approach to assess human settlement sprawl using labeled multispectral satellite image patches and Convolutional Neural Networks (CNN). By training deep learning classifiers with a dataset of 5,359,442 records consisting of satellite images and census data from 2010, we evaluate sprawl for settlements across the country. The study focuses on major cities in Mexico, comparing ground truth results for 2015 and 2020. EfficientNet-B7 achieved the best performance among various CNN architectures evaluated with an ROC AUC of 0.970 and a PR AUC of 0.972. To assess human settlement sprawl, we introduce an information-based metric that offers advantages over entropy-based alternatives. We demonstrate its application to major cities in Mexico as examples.

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