Visual Perception of Building and Household Vulnerability from Streets

• URL: http://arxiv.org/abs/2205.14460v1
• Date: Sat, 28 May 2022 15:35:47 GMT
• Title: Visual Perception of Building and Household Vulnerability from Streets
• Authors: Chaofeng Wang, Sarah Elizabeth Antos, Jessica Grayson Gosling Goldsmith, Luis Miguel Triveno
• Abstract summary: In developing countries, building codes often are outdated or not enforced. A large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. We propose an evaluation framework that is cost-efficient for first capture and future updates.
• Score: 0.294944680995069
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In developing countries, building codes often are outdated or not enforced. As a result, a large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. Assessing housing quality is key to inform public policies and private investments. Standard assessment methods are typically carried out only on a sample / pilot basis due to its high costs or, when complete, tend to be obsolete due to the lack of compliance with recommended updating standards or not accessible to most users with the level of detail needed to take key policy or business decisions. Thus, we propose an evaluation framework that is cost-efficient for first capture and future updates, and is reliable at the block level. The framework complements existing work of using street view imagery combined with deep learning to automatically extract building information to assist the identification of housing characteristics. We then check its potential for scalability and higher level reliability. For that purpose, we create an index, which synthesises the highest possible level of granularity of data at the housing unit and at the household level at the block level, and assess whether the predictions made by our model could be used to approximate vulnerability conditions with a lower budget and in selected areas. Our results indicated that the predictions from the images are clearly correlated with the index.

Related papers

• A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam [1.2289361708127877]
  This paper investigates the spatial distribution of utility derived from street-level conditions in residential location choices on a city-wide scale. We find that the utility derived from street-level conditions varies significantly on a highly localised scale. Our results also reveal that the high real-estate prices in the city centre cannot be attributed to attractive street-level conditions.
  arXiv  Detail & Related papers  (2024-10-23T13:52:22Z)
• Beyond Eviction Prediction: Leveraging Local Spatiotemporal Public Records to Inform Action [11.183319154396369]
  We perform an eviction prediction task to produce risk scores and then use these risk scores to plan targeted outreach policies. We show that the risk scores are, in fact, useful, enabling a theoretical team of caseworkers to reach more eviction-prone properties in the same amount of time. We also discuss the importance of neighborhood and ownership features in both risk prediction and targeted outreach.
  arXiv  Detail & Related papers  (2024-01-27T09:29:11Z)
• Fine-Grained Property Value Assessment using Probabilistic Disaggregation [14.618878494135226]
  We propose a method to estimate the distribution over property value at the pixel level from remote sensing imagery. We evaluate on a real-world dataset of a major urban area.
  arXiv  Detail & Related papers  (2023-05-31T23:40:47Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• Post-Contextual-Bandit Inference [57.88785630755165]
  Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking. They can both improve outcomes for study participants and increase the chance of identifying good or even best policies. To support credible inference on novel interventions at the end of the study, we still want to construct valid confidence intervals on average treatment effects, subgroup effects, or value of new policies.
  arXiv  Detail & Related papers  (2021-06-01T12:01:51Z)
• RobustBench: a standardized adversarial robustness benchmark [84.50044645539305]
  Key challenge in benchmarking robustness is that its evaluation is often error-prone leading to robustness overestimation. We evaluate adversarial robustness with AutoAttack, an ensemble of white- and black-box attacks. We analyze the impact of robustness on the performance on distribution shifts, calibration, out-of-distribution detection, fairness, privacy leakage, smoothness, and transferability.
  arXiv  Detail & Related papers  (2020-10-19T17:06:18Z)
• Predicting Livelihood Indicators from Community-Generated Street-Level Imagery [70.5081240396352]
  We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
  arXiv  Detail & Related papers  (2020-06-15T18:12:12Z)
• Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning [70.01650994156797]
  Off- evaluation of sequential decision policies from observational data is necessary in batch reinforcement learning such as education healthcare. We develop an approach that estimates the bounds of a given policy. We prove convergence to the sharp bounds as we collect more confounded data.
  arXiv  Detail & Related papers  (2020-02-11T16:18:14Z)
• Generating Interpretable Poverty Maps using Object Detection in Satellite Images [80.35540308137043]
  We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
  arXiv  Detail & Related papers  (2020-02-05T02:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.