Building Height Estimation Using Shadow Length in Satellite Imagery
Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as an additional cue to compensate for the loss of building height estimation using single-view imagery. We proposed a novel method that first localized a building and its shadow in the given satellite image. After localization, the shadow length is estimated using a regression model. To estimate the final height of each building, we utilize the principles of photogrammetry, specifically considering the relationship between the solar elevation angle, the vertical edge length of the building, and the length of the building's shadow. For the localization of buildings in our model, we utilized a modified YOLOv7 detector, and to regress the shadow length for each building we utilized the ResNet18 as backbone architecture. Finally, we estimated the associated building height using solar elevation with shadow length through analytical formulation. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin.
Code (1)
Similar Papers 제목 키워드 기반
Building Height Prediction with Instance Segmentation
Extracting building heights from satellite images is an active research area used in many fields such as telecommunications, city planning, etc. Many studies utilize DSM (Digital Surface Models) generated with lidars or …
Instance SegmentationPredictionSegmentationSemantic Segmentation+1A CNN regression model to estimate buildings height maps using Sentinel-1 SAR and Sentinel-2 MSI time series
Accurate estimation of building heights is essential for urban planning, infrastructure management, and environmental analysis. In this study, we propose a supervised Multimodal Building Height Regression Network (MBHR-N…
ManagementTime SeriesSpatially explicit feature importance for building height estimation using research-access high-resolution SAR and optical sensors
Accurate building height information at the individual footprint scale is essential for material stock accounting and post-disaster damage assessments yet remains difficult to obtain at city scale in the Global South whe…
Feature ImportanceTEMPO: Global Temporal Building Density and Height Estimation from Satellite Imagery
We present TEMPO, a global, temporally resolved dataset of building density and height derived from high-resolution satellite imagery using deep learning models. We pair building footprint and height data from existing d…
Data Fusion for Multi-Task Learning of Building Extraction and Height Estimation
In accordance with the urban reconstruction problem proposed by the DFC23 Track 2 Contest, this paper attempts a multitask-learning method of building extraction and height estimation using both optical and radar satelli…
Multi-Task Learning