Beyond the First Law of Geography: Learning Representations of Satellite Imagery by Leveraging Point-of-Interests

Xi, Yanxin; Li, Tong; Wang, Huandong; Li, Yong; Tarkoma, Sasu; Hui, Pan

doi:10.1145/3485447.3512149

Cited by 24 publications

(9 citation statements)

References 27 publications

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“…OpenStreetMap is a collaborative project that collectively generates spatial databases. The current study posits that the distance to the reference points under consideration is contingent upon the hypothesis that the built environments of these locations are associated with the prevailing socioeconomic conditions Hu et al (2016); Li and Liu (2019); Niu et al (2020); Xi et al (2022); Ye et al 2011Ye et al , 2019. These points of interest, as identified in this study, are envisaged to serve as reliable indicators of the spatial distribution of socioeconomic status.…”

Section: Openstreetmaps (Osm)mentioning

confidence: 97%

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

Muñetón-Santa,

Manrique-Ruiz

2023

Social Sciences

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This paper presents a methodology to estimate the multidimensional poverty index using spatial data at the street block level. The data used in this study were obtained from Open Street Maps and ESA’s land use cover, which are freely available sources of spatial information. The study employs five machine-learning algorithms, including Catboost, Lightboost, and Random Forest, to estimate the multidimensional poverty index with spatial granularity. The results indicate that these models achieve promising performance in predicting poverty levels in Medellín, Colombia. The results showed that the Random Forest algorithm achieved the highest performance, with an MAE of 0.07504. Furthermore, the spatial distribution of the multidimensional poverty estimate was highly correlated with the true values of the distribution. This work contributes to predicting multidimensional poverty by demonstrating the potential of machine learning algorithms to utilize accessible spatial data. By providing evidence of the feasibility of estimating poverty levels at a granular spatial level, this methodology offers a powerful tool for policymakers to make poverty social interventions with low-cost evidence. Furthermore, this study has important implications for poverty eradication efforts in developing countries, where access to reliable data remains challenging.

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Section: Openstreetmaps (Osm)mentioning

confidence: 97%

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

Muñetón-Santa,

Manrique-Ruiz

2023

Social Sciences

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“…Metrics and Implementation Three evaluation metrics commonly used for evaluating indicator prediction are selected: coefficient of determination (R 2 ), mean absolute error (MAE), and rooted mean squared error (RMSE) (Han et al 2020;Jean et al 2016;Xi et al 2022). We apply Adam optimizer for parameter learning and perform a grid search on all hyperparameters of both our model and baselines, with a search range of the number of graph convolutional layers in {2, 3, 4}, learning rate in [1e-4, 5e-2], and batch size in {8, 16, 32, 64}.…”

Section: Baselines Carbon Prediction Machinementioning

confidence: 99%

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Zeng,

Liu,

Ding

et al. 2024

AAAI

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Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

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“…To the best of our knowledge, this is the first work to identify regional epidemic exposure risks through street view imagery. We adapt three commonly used paradigms in socioeconomic prediction task to validate the proposed method: feature based baselines (BOF [12], SceneParse [6]), end-to-end supervised CV baselines (ResNet18 [5], ViT-B/32 [2]), and unsupervised baselines (Urban2vec [13], READ [3], PG-SimCLR [14]). Implementation details are summarized as below.…”

Section: Baseline Modelsmentioning

confidence: 99%

“…• READ [3]: A semi-supervised model using a pretrained CV model that fine-tuned on downstream tasks using data pruning and dimensionality reduction technology. • PG-SimCLR [14]: An unsupervised model that use geographical distance and POI similarity to construct positive and negative image pairs and use attention module to fuse the embeddings.…”

Section: Baseline Modelsmentioning

confidence: 99%

Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery

Han,

Xi,

Xia

et al. 2023

Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

Self Cite

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Built environment supports all the daily activities and shapes our health. Leveraging informative street view imagery, previous research has established the profound correlation between the built environment and chronic, non-communicable diseases; however, predicting the exposure risk of infectious diseases remains largely unexplored. The person-to-person contacts and interactions contribute to the complexity of infectious disease, which is inherently different from non-communicable diseases. Besides, the complex relationships between street view imagery and epidemic exposure also hinder accurate predictions. To address these problems, we construct a regional mobility graph informed by the gravity model, based on which we propose a transmission-aware graph convolutional network (GCN) to capture disease transmission patterns arising from human mobility. Experiments show that the proposed model significantly outperforms baseline models by 8.54% in weighted F1, shedding light on a low-cost, scalable approach to assess epidemic exposure risks from street view imagery. CCS CONCEPTS• Applied computing → Life and medical sciences; • Computing methodologies → Computer vision; Machine learning.

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Beyond the First Law of Geography: Learning Representations of Satellite Imagery by Leveraging Point-of-Interests

Cited by 24 publications

References 27 publications

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

Predicting Multidimensional Poverty with Machine Learning Algorithms: An Open Data Source Approach Using Spatial Data

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery

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