A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas

Tan, Xiaoyong; Deng, Min; Chen, Kaiqi; Shi, Yan; Zhao, Bingbing; Liu, Qinghao

doi:10.1080/15481603.2023.2290352

Cited by 4 publications

(2 citation statements)

References 60 publications

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“…There are several models to predict LULC, such as statistical models [34], the Markov chain model [35], logistic regression [36], Cellular Automata (CA) [37], and Artificial Neural Networks (ANNs) [38]. CA is a commonly used method for urban growth modeling [39,40] and has an open structure, which makes it easier to integrate with other models [41]. It also has a strong spatial computing power to simulate spatial variability [42,43].…”

Section: Introductionmentioning

confidence: 99%

Impact of Land Use and Land Cover Change on Hydrological Processes in Urban Watersheds: Analysis and Forecasting for Flood Risk Management

Banjara,

Bhusal,

Ghimire

et al. 2024

Geosciences

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Land use and land cover (LULC) change is one of the primary contributors to hydrological change in urban watersheds and can potentially influence stream flow and flood volume. Understanding the impacts of LULC change on urban hydrological processes is critical to effective urban water management and minimizing flood risks. In this context, this study aims to determine the impacts of LULC change on hydrological response in a fast transitioning watershed for the predicted years of 2050 and 2080. This research employs the hybrid land use classification technique, Cellular Automata–Markov (CA–Markov) model to predict land use changes, utilizing land use data from 2001, 2013, and 2021. Additionally, it incorporates a calibrated, event-specific hydrologic model known as the Personal Computer Storm Water Management Model (PCSWMM) to assess alterations in hydrological responses for storm events of various magnitudes. The findings indicate a transition of the watershed into an urbanized landscape, replacing the previous dominance of agriculture and forested areas. The initial urban area, constituting 11.6% of the total area in 2021, expands to cover 34.1% and 44.2% of the total area by 2050 and 2080, respectively. Due to the LULC changes, there are increases in peak discharge of 5% and 6.8% and in runoff volume of 8% and 13.3% for the years 2050 and 2080 for a 100-year return period storm event. Yet, the extent of these changes intensifies notably during storm events with lower return periods. This heightened impact is directly attributed to the swift urbanization of the watershed. These results underscore the pressing necessity to regulate LULC change to preserve the hydrological equilibrium.

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Section: Introductionmentioning

confidence: 99%

Impact of Land Use and Land Cover Change on Hydrological Processes in Urban Watersheds: Analysis and Forecasting for Flood Risk Management

Banjara,

Bhusal,

Ghimire

et al. 2024

Geosciences

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“…Remote sensing data have been applied to agriculture [11][12][13], forestry [14,15], meteorology [16,17], and other fields successfully, including images from satellites [18,19] and unmanned aerial vehicles (UAVs) [20,21]. Remote sensing has the advantages of wide observation range, fast speed, and short cycle of obtaining data with high spatial resolution, so detecting landslides with remote sensing technology has become a trend [22].…”

Section: Introductionmentioning

confidence: 99%

Optimal and Multi-View Strategic Hybrid Deep Learning for Old Landslide Detection in the Loess Plateau, Northwest China

Gao,

Xi,

et al. 2024

Remote Sensing

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Old landslides in the Loess Plateau, Northwest China usually occurred over a relatively long period, and their sizes are usually smaller compared to old landslides in the alpine valley areas of Sichuan, Yunnan, and Southeast Tibet. These landslide areas may have been changed either partially or greatly, and they are usually covered with vegetation and similar to their surrounding environment. Therefore, it is a great challenge to detect them using high-resolution remote sensing images with only orthophoto view. This paper proposes the optimal-view and multi-view strategic hybrid deep learning (OMV-HDL) method for old loess landslide detection. First, the optimal-view dataset in the Yan’an area (YA-OP) was established to solve the problem of insufficient optical features in orthophoto images. Second, in order to make the process of interpretation more labor-saving, the optimal-view and multi-view (OMV) strategy was proposed. Third, hybrid deep learning with weighted boxes fusion (HDL-WBF) was proposed to detect old loess landslides effectively. The experimental results with the constructed optimal-view dataset and multi-view data show that the proposed method has excellent performance among the compared methods—the F1 score and AP (mean) of the proposed method were improved by about 30% compared with the single detection model using traditional orthophoto-view data—and that it has good detection performance on multi-view data with the recall of 81.4%.

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The Geographic Automata Tool: A New General-Purpose Geosimulation Extension for ArcGIS Pro

van Duynhoven,

Dragićević

2024

Applied Sciences

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The theoretical paradigm of geographic automata systems (GAS) underpins a wide range of studies to represent dynamic complex geospatial phenomena. Specifically, cellular automata (CA) were used extensively over the past 40 years for geospatial applications, though primarily for modeling urban growth. Currently, the hyper-specialized and fragmented geospatial technology ecosystem supporting CA model implementation often necessitates programmed solutions or use of disconnected programs with graphical user interfaces (GUIs) separate from common geographic information systems (GIS) software. Therefore, the main goal of this study is to present a general-purpose, GIS-based CA modeling framework and extension for Esri’s ArcGIS Pro software environment. The methodological approach centered around (1) developing generic functions for building binary or multi-class CA models to capture various spatiotemporal systems and (2) enabling end-to-end CA modeling projects that can incorporate built-in functionality available in ArcGIS Pro versions 3.1 and newer. Two case studies demonstrate the add-in capabilities to support geosimulation model-building activities and exploration of new hybrid models. This research contributes to advancing flexible, transparent spatiotemporal modeling tools within existing GIS software. The proposed approach addresses the lack of streamlined geospatial technologies capable of simulating numerous dynamic geospatial phenomena, exploring human and environmental processes, and examining possible futures with CA in research, decision making, or educational settings.

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A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas

Cited by 4 publications

References 60 publications

Impact of Land Use and Land Cover Change on Hydrological Processes in Urban Watersheds: Analysis and Forecasting for Flood Risk Management

Impact of Land Use and Land Cover Change on Hydrological Processes in Urban Watersheds: Analysis and Forecasting for Flood Risk Management

Optimal and Multi-View Strategic Hybrid Deep Learning for Old Landslide Detection in the Loess Plateau, Northwest China

The Geographic Automata Tool: A New General-Purpose Geosimulation Extension for ArcGIS Pro

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