Integrating Vision Language and Foundation Models for Automated Estimation of Building Lowest Floor Elevation from Street View Imagery

Core Concepts

This study integrates the Segment Anything model, a segmentation foundation model, with vision language models to conduct text-prompt image segmentation on street view images for automated estimation of building lowest floor elevation (LFE). The proposed method significantly enhances the availability of LFE estimation compared to the existing model.

Abstract

The study aims to address the challenges in LFE estimation using image segmentation by integrating the Segment Anything model (SAM) with vision language models. It evaluates various vision language models, integration methods, and text prompts to identify the most suitable model for street view image analytics and LFE estimation tasks. The key highlights and insights are: Evaluation of five text-prompt segmentation methods: GDINO-SAM, which uses Grounding DINO to generate box prompts for SAM, achieves the highest IoU (75.63%) and fastest inference speed (1.33 FPS), outperforming other methods. The prompt-triggered approach, where the vision language model precedes SAM, demonstrates greater efficiency compared to the prompt-filtered approach. Text prompt selection: The text prompt "the door in the front of the house" is identified as the most effective in distinguishing the front door from other doors like garage doors. Integration with LFE estimation: The proposed ELEV-VISION-SAM model, which integrates GDINO-SAM with the selected text prompt, significantly enhances the availability of LFE estimation from 33.25% to 55.99% compared to the baseline ELEV-VISION model. ELEV-VISION-SAM achieves comparable mean absolute error (0.22 m) to ELEV-VISION (0.19 m) while covering 98.71% of houses with visible front doors. The study presents a novel computational approach for vertical feature extraction from street view images using text-prompt segmentation, which can be applied to various civil engineering and infrastructure analytics tasks beyond LFE estimation.

Stats

The lowest floor of a building refers to the lowest floor of the lowest enclosed area, excluding enclosures used for parking, building access, storage, or flood-resistance. The traditional LFE measuring method is on-site manual inspection using a total station theodolite, which incurs significant costs in terms of time, finances, and human resources. Street view imagery has emerged as a valuable resource for urban analytics research, offering a more scalable alternative to traditional on-site measurements for LFE estimation.

Quotes

"Street view imagery, aided by advancements in image quality and accessibility, has emerged as a valuable resource for urban analytics research." "Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on-site measurements, crucial for assessing properties' flood risk and damage extent." "Remarkably, our proposed method significantly enhances the availability of LFE estimation to almost all properties in which the front door is visible in the street view image."

Key Insights Distilled From

ELEV-VISION-SAM: Integrated Vision Language and Foundation Model for Automated Estimation of Building Lowest Floor Elevation

by Yu-Hsuan Ho,... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12606.pdf

Deeper Inquiries

How can the proposed method be extended to incorporate additional building features beyond the front door for further improving the accuracy and availability of LFE estimation

The proposed method can be extended to incorporate additional building features beyond the front door by leveraging advanced image segmentation techniques and integrating multiple vision models. One approach could involve utilizing object detection algorithms to identify and segment various building elements such as windows, roofs, and architectural details. By incorporating these additional features into the segmentation process, the model can create a more comprehensive understanding of the building structure, leading to improved accuracy in LFE estimation. Furthermore, the integration of semantic segmentation models can help differentiate between different building components and enhance the segmentation of complex structures. By training the model to recognize and segment specific architectural elements, such as balconies, chimneys, or decorative facades, the accuracy of LFE estimation can be further enhanced. Additionally, incorporating depth estimation techniques can provide valuable spatial information about the building structure, enabling more precise elevation calculations for LFE estimation. Moreover, the use of multi-modal data fusion, combining street view images with other geospatial data sources such as LiDAR or building blueprints, can enrich the feature extraction process. By integrating data from various sources, the model can gain a more comprehensive understanding of the building environment, leading to more accurate and reliable LFE estimations. Overall, by expanding the scope of features considered in the segmentation process, the proposed method can significantly improve the accuracy and availability of LFE estimation for a wide range of building types and conditions.

What are the potential limitations of using low-resolution depthmaps associated with street view images, and how can novel techniques for extracting depth information directly from the images be explored to address these limitations

The use of low-resolution depthmaps associated with street view images poses several limitations that can impact the accuracy and reliability of LFE estimation. One major limitation is the reduced spatial resolution of depth information, which can lead to inaccuracies in distance measurements and depth perception. Low-resolution depthmaps may struggle to capture fine details and subtle variations in elevation, especially in complex building structures or challenging environmental conditions. To address these limitations, novel techniques for extracting depth information directly from street view images can be explored. One approach is to leverage advanced computer vision algorithms, such as structure-from-motion (SfM) or stereo vision techniques, to estimate depth information from image pairs or sequences. By analyzing the parallax effect and image disparities, these techniques can generate more detailed and accurate depth maps, enhancing the spatial understanding of the scene. Additionally, the integration of depth estimation models based on neural networks, such as monocular depth estimation networks or depth from single images (DfSI) models, can provide more robust depth information directly from the street view images. These deep learning-based approaches can learn complex depth cues and spatial relationships from the images, enabling more accurate depth estimation even in low-resolution or challenging scenarios. Furthermore, the fusion of depth information from multiple sources, such as LiDAR data or aerial imagery, can enhance the depth estimation process and improve the overall quality of depthmaps associated with street view images. By combining data from diverse sensors and modalities, the model can overcome the limitations of low-resolution depthmaps and generate more reliable depth information for LFE estimation tasks.

How can the presented computational methodology for text-prompt image segmentation be applied to other vertical feature extraction tasks in civil engineering and infrastructure analytics, such as structural anomaly detection in bridges or electrical infrastructure damage assessment

The presented computational methodology for text-prompt image segmentation can be applied to various vertical feature extraction tasks in civil engineering and infrastructure analytics, offering valuable insights and advancements in these domains. One key application is structural anomaly detection in bridges, where the model can be trained to segment and analyze bridge components from street view images. By identifying structural anomalies, such as cracks, deformations, or corrosion, the model can assist in assessing the condition of bridges and prioritizing maintenance or repair efforts. Moreover, the methodology can be utilized for electrical infrastructure damage assessment, particularly in evaluating power line sag, pole condition, or equipment damage. By segmenting and analyzing electrical infrastructure components from street view images, the model can help identify potential issues, assess the extent of damage, and support decision-making for maintenance and repair activities. Additionally, the computational approach can be applied to subsidence detection in properties, leveraging historical street view images to analyze changes in building elevation over time. By segmenting and comparing building features across different time points, the model can detect subsidence patterns, assess property stability, and support risk mitigation strategies. Furthermore, the methodology can be extended to urban planning and development projects, where it can assist in architectural design space interpretation, construction site safety management, and infrastructure planning. By segmenting and analyzing urban features from street view images, the model can provide valuable insights for urban planners, engineers, and policymakers to optimize urban environments, enhance infrastructure resilience, and improve overall city functionality. Overall, the computational methodology for text-prompt image segmentation offers a versatile and powerful tool for a wide range of vertical feature extraction tasks in civil engineering and infrastructure analytics.