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

arXiv: https://arxiv.org/abs/2404.12606 · HTML: https://mlsanity.com/paper/2404.12606/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [Vision-Language Models for Vision Tasks: A Survey](https://mlsanity.com/paper/2304.00685/) (2023)
- [Towards Open Vocabulary Learning: A Survey](https://mlsanity.com/paper/2306.15880/) (2023)
- [Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models](https://mlsanity.com/paper/2304.10597/) (2023)
- [A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data](https://mlsanity.com/paper/2006.09201/) (2020)
- [ELEV-VISION: Automated Lowest Floor Elevation Estimation from Segmenting Street View Images](https://mlsanity.com/paper/2306.03050/) (2023)
- [Unsupervised Graph Deep Learning Reveals Emergent Flood Risk Profile of Urban Areas](https://mlsanity.com/paper/2309.14610/) (2023)
- [FloodGenome: Interpretable Machine Learning for Decoding Features Shaping Property Flood Risk Predisposition in Cities](https://mlsanity.com/paper/2403.10625/) (2024)
- [Automated building image extraction from 360deg panoramas for postdisaster evaluation](https://mlsanity.com/paper/1905.01524/) (2019)

## Built on by (most cited first, 9 of 9)

- [Automated Wildfire Damage Assessment from Multi view Ground level Imagery Via Vision Language Models](https://mlsanity.com/paper/2509.01895/) (2025)
- [Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors](https://mlsanity.com/paper/2503.00610/) (2025)
- [Flood-DamageSense: Multimodal Mamba with Multitask Learning for Building Flood Damage Assessment using SAR Remote Sensing Imagery](https://mlsanity.com/paper/2506.06667/) (2025)
- [CrisiSense-RAG: Crisis Sensing Multimodal Retrieval-Augmented Generation for Rapid Disaster Impact Assessment](https://mlsanity.com/paper/2602.13239/) (2026)
- [Recov-Vision: Linking Street View Imagery and Vision-Language Models for Post-Disaster Recovery](https://mlsanity.com/paper/2509.20628/) (2025)
- [High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data](https://mlsanity.com/paper/2409.13936/) (2024)
- [Training-free retrieval-augmented generation with reinforced reasoning for flood damage nowcasting](https://mlsanity.com/paper/2602.10312/) (2026)
- [Property-Level Flood Risk Assessment Using AI-Enabled Street-View Lowest Floor Elevation Extraction and ML Imputation Across Texas](https://mlsanity.com/paper/2604.01153/) (2026)
- [Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery](https://mlsanity.com/paper/2608.17165/) (2026)

## Abstract (arXiv)

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. While existing methods rely on object detection, the introduction of image segmentation has broadened street view images' utility for LFE estimation, although challenges still remain in segmentation quality and capability to distinguish front doors from other doors. To address these challenges in LFE estimation, 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 LFE estimation. By evaluating various vision language models, integration methods, and text prompts, we identify the most suitable model for street view image analytics and LFE estimation tasks, thereby improving the availability of the current LFE estimation model based on image segmentation from 33% to 56% of properties. 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. Also the findings present the first baseline and comparison of various vision models of street view image-based LFE estimation. The model and findings not only contribute to advancing street view image segmentation for urban analytics but also provide a novel approach for image segmentation tasks for other civil engineering and infrastructure analytics tasks.