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ISPRS Int. J. Geo-Inf., Volume 13, Issue 10 (October 2024) – 6 articles

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18 pages, 9353 KiB

Open AccessArticle

Sky-Scanning for Energy: Unveiling Rural Electricity Consumption Patterns through Satellite Imagery’s Convolutional Features

by Yaofu Huang, Weipan Xu, Dongsheng Chen, Qiumeng Li, Weihuan Deng and Xun Li

ISPRS Int. J. Geo-Inf. 2024, 13(10), 345; https://doi.org/10.3390/ijgi13100345 (registering DOI) - 26 Sep 2024

Abstract

The pursuit of the Sustainable Development Goals has highlighted rural electricity consumption patterns, necessitating innovative analytical approaches. This paper introduces a novel method for predicting rural electricity consumption by leveraging deep convolutional features extracted from satellite imagery. The study employs a pretrained remote sensing interpretation model for feature extraction, streamlining the training process and enhancing the prediction efficiency. A random forest model is then used for electricity consumption prediction, while the SHapley Additive exPlanations (SHAP) model assesses the feature importance. To explain the human geography implications of feature maps, this research develops a feature visualization method grounded in expert knowledge. By selecting feature maps with higher interpretability, the “black-box” model based on remote sensing images is further analyzed and reveals the geographical features that affect electricity consumption. The methodology is applied to villages in Xinxing County, Guangdong Province, China, achieving high prediction accuracy with a correlation coefficient of 0.797. The study reveals a significant positive correlations between the characteristics and spatial distribution of houses and roads in the rural built environment and electricity demand. Conversely, natural landscape elements, such as farmland and forests, exhibit significant negative correlations with electricity demand predictions. These findings offer new insights into rural electricity consumption patterns and provide theoretical support for electricity planning and decision making in line with the Sustainable Development Goals. Full article

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33 pages, 24105 KiB

Open AccessArticle

Pre-Dam Vltava River Valley—A Case Study of 3D Visualization of Large-Scale GIS Datasets in Unreal Engine

by Michal Janovský

ISPRS Int. J. Geo-Inf. 2024, 13(10), 344; https://doi.org/10.3390/ijgi13100344 - 26 Sep 2024

Abstract

This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km² and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km² to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas. Full article

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14 pages, 2387 KiB

Open AccessReview

The Status of the Implementation of the Building Information Modeling Mandate in Poland: A Literature Review

by Andrzej Szymon Borkowski, Wojciech Drozd and Krzysztof Zima

ISPRS Int. J. Geo-Inf. 2024, 13(10), 343; https://doi.org/10.3390/ijgi13100343 - 26 Sep 2024

Abstract

BIM is being strongly implemented in design companies. General contractors are using it during investment projects, and boards are using it for the maintenance and operation of buildings or infrastructure. Without the so-called BIM mandate (mandatory in public procurement), this is hard to imagine, even though it has already been implemented in many countries. In Poland, work in this direction is still being carried out. Due to the high complexity of investment and construction processes, the multiplicity of stakeholder groups, and conflicting interests, work on BIM adoption at the national level is hampered. The paper conducts an in-depth literature review of BIM implementation in Poland and presents a critical analysis of the current state of work. As a result of the literature research, proposals for changes in the processes of implementing the BIM mandate in Poland were formulated. This paper presents an excerpt from a potential BIM strategy and the necessary steps on the road to making BIM use mandatory. The results of the study indicate strong grassroots activity conducted by NGOs, which, independent of government actions, lead to measurable results. The authors propose that these activities must be coordinated by a single leading entity at the government level. The study could influence decisions made in other countries in the region or with similar levels of BIM adoption. BIM is the basis of the idea of the digital twin, and its implementation is necessary to achieve the goals of the doctrine of sustainable development and circular economy. Full article

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18 pages, 3496 KiB

Open AccessArticle

Analysis of Guidance Signage Systems from a Complex Network Theory Perspective: A Case Study in Subway Stations

by Fei Peng, Zhe Zhang and Qingyan Ding

ISPRS Int. J. Geo-Inf. 2024, 13(10), 342; https://doi.org/10.3390/ijgi13100342 - 25 Sep 2024

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Abstract

Guidance signage systems (GSSs) play a large role in pedestrian navigation for public buildings. A vulnerable GSS can cause wayfinding troubles for pedestrians. In order to investigate the robustness of GSSs, a complex network-based GSS robustness analysis framework is proposed in this paper. First, a method that can transform a GSS into a guidance service network (GSN) is proposed by analyzing the relationships among various signs, and signage node metrics are proposed to evaluate the importance of signage nodes. Second, two network performance metrics, namely, the level of visibility and guidance efficiency, are proposed to evaluate the robustness of the GSN under various disruption modes, and the most important signage node metrics are determined. Finally, a multi-objective optimization model is established to find the optimal weights of these metrics, and a comprehensive evaluation method is proposed to position the critical signage nodes that should receive increased maintenance efforts. A case study was conducted in a subway station and the GSS was transformed into a GSN successfully. The analysis results show that the GSN has scale-free characteristics, and recommendations for GSS design are proposed on the basis of robustness analysis. The signage nodes with high betweenness centrality play a greater role in the GSN than the signage nodes with high degree centrality. The proposed critical signage node evaluation method can be used to efficiently identify the signage nodes for which failure has the greatest effects on GSN performance. Full article

(This article belongs to the Topic 3D Computer Vision and Smart Building and City, 2nd Volume)

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20 pages, 5641 KiB

Open AccessArticle

Urban Spatiotemporal Event Prediction Using Convolutional Neural Network and Road Feature Fusion Network

by Yirui Jiang, Shan Zhao, Hongwei Li, Huijing Wu and Wenjie Zhu

ISPRS Int. J. Geo-Inf. 2024, 13(10), 341; https://doi.org/10.3390/ijgi13100341 - 25 Sep 2024

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Abstract

The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R² value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods. Full article

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17 pages, 40944 KiB

Open AccessArticle

Enhancing Agricultural Productivity: Integrating Remote Sensing Techniques for Cotton Yield Monitoring and Assessment

by Amil Aghayev, Tomáš Řezník and Milan Konečný

ISPRS Int. J. Geo-Inf. 2024, 13(10), 340; https://doi.org/10.3390/ijgi13100340 - 24 Sep 2024

Viewed by 340

Abstract

This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P₂O₅), potassium (K₂O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security. Full article

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