The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation
Authors: Yanjiao Song, Bowen Cai, Timo Balz, Zhenfeng Shao, Neema Simon Sumari, James Magidi, Walter Musakwa
Organizations: aState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan, 430079, Hubei, China · bDepartment of Geomatics Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, 60111, East Java, Indonesia · cDepartment of Informatics and Information Technology, College of Natural and Applied Sciences (CoNAS), Sokoine University of Agriculture, Morogoro, 3038, Morogoro, Tanzania · dGeomatics Department, Tshwane University of Technology, Pretoria, 0183, Gauteng, South Africa · eDepartment of Geography, Environmental Management and Energy Studies, Faculty of Science, University of Johannesburg, Johannesburg, 2006, Gauteng, South Africa
Abstract
Monocular building height estimation from optical imagery is important for characterizing urban vertical structure, yet remains challenging due to the heterogeneity of urban building morphology and the indirect relationship between optical image appearance and building height. The recently launched PhiSat-2 satellite provides a promising open-access data source for this task, with 4.75m spatial resolution and seven multispectral bands spanning the visible to near-infrared range. However, its suitability for monocular building height estimation has not been systematically assessed. This study presents an initial open-reference assessment of PhiSat-2 imagery for this task by constructing a PhiSat-2--Height Dataset (PHDataset) and proposing a Two-Stream Ordinal Network (TSONet). PHDataset integrates global PhiSat-2 imagery with open building-height references and contains 9,475 co-registered patch pairs from 26 cities worldwide. TSONet jointly learns dense height estimation and auxiliary footprint prediction, using footprint-aware structural guidance and ordinal height modeling to better exploit PhiSat-2 spatial--spectral information. Specifically, a Cross-Stream Exchange Module (CSEM) enables adaptive interaction between the height and footprint streams, while a Feature-Enhanced Bin Refinement (FEBR) module performs coarse-to-fine ordinal query refinement with multi-level features. Experiments on PHDataset show that TSONet outperforms representative competing methods, reducing MAE and RMSE by over 13.2% and 9.7%, respectively, while improving IoU and F1-score by over 14.0% and 10.1%. Additional analyses further indicate that PhiSat-2 imagery contains useful spatial--spectral cues for monocular building height estimation at an intermediate spatial resolution.
Building height (BH) and building footprint (BF) jointly describe the vertical and horizontal extent of the built environment and are required inputs for urban climate, disaster-risk, and population-mapping models. The two parameters are coupled through floor-area-ratio (FAR) constraints, yet remote-sensing approaches typically treat them as independent regression targets. We argue that explicitly encoding this cross-task coupling is more impactful than further refining individual encoders, and propose MorphoFormer, a joint BH/BF estimation framework built around two complementary mechanisms: (i) a BF-Guided Task Decoder (BGTD) that gates the height branch via cross-attention on a footprint-derived morphology context, and (ii) a Morphology Consistency Loss (MCL) that supervises a height-from-footprint surrogate against the ground-truth BH, indirectly forcing the BF feature to encode height-correlated structure. The encoder is a single-stage Swin backbone fed by Sentinel-1 SAR, Sentinel-2 multispectral, and DEM inputs, trained and evaluated on a geo-blocked split of 54 cities. Against a Swin-MTL baseline at identical receptive field, MorphoFormer reduces BH test RMSE from 3.39 to 3.15 m (R^2 improves 0.62 -> 0.67) with BF R^2 stable at 0.80. Controlled ablations at identical capacity attribute most of this 0.24 m improvement to the two proposed mechanisms: removing BGTD raises BH RMSE by 0.11 m and removing MCL raises it by 0.11 m, with the residual approximately 0.02 m falling within the noise floor of encoder-side variations. Because both mechanisms act on cross-task representations rather than pixels, the design carries no intrinsic dependence on input resolution.
We present a multi-modal classification framework that fuses satellite and street-level imagery through a Perceiver IO architecture operating on spatial patch tokens from a shared DINOv2 backbone. The design naturally handles a variable number of street-level views per building without padding or fixed-size pooling, and jointly predicts multi-label roof element and roof material classes. We construct a large-scale dataset of 32,135 buildings (61,672 segments) spanning ten countries, pairing satellite images with up to eight street-level views per segment and evaluating four masking strategies for isolating the target building. We propose an RGB-M masking strategy that appends the building footprint mask as a fourth input channel, providing a soft spatial prior that outperforms hard cropping across both modalities. The Perceiver IO fusion model improves over all other fusion strategies and yields substantial per-class gains for attributes visible from street level (e.g., +11.3 AP for slate, +1.3 AP for dormers), though the satellite-only baseline retains a slight advantage in macro-averaged mAP for classes that are predominantly visible from above. These results establish a scalable, flexible architecture for multi-modal building inspection that can accommodate heterogeneous inputs and multiple output tasks.
Niels Sombekke, Rob G. J. Wijnhoven, Martin R. Oswald
Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; however, its native 10m resolution makes building vs non-building classification challenging, particularly for small or sub-pixel buildings, and performance can vary with both seasonality and the heterogeneity of built-up environments. This paper introduces a Sentinel-2 building-detection framework designed to systematically quantify these effects and to support more formalised, practice-oriented model selection. We construct a dedicated multi-temporal Sentinel-2 dataset over the Warsaw region and derive binary ground-truth masks by rasterising official Polish topographic database (BDOT10k) building footprints onto the Sentinel-2 pixel grid. Using two established convolutional segmentation backbones (U-Net and DeepLabV3+), we first perform scene-specific fine-tuning to select a robust architecture and identify the best monthly models for L1C and L2A products separately. We then conduct cross-temporal inference by applying each best monthly model to all scenes, enabling an assessment of (i) which months provide favourable training and inference conditions, (ii) how performance transfers between seasons, (iii) the impact of processing level, and (iv) how these effects differ across built-up typologies. Based on these results, we provide practical guidance for routine Sentinel-2 building classification under varying acquisition periods and settlement characteristics.
Michał Romaszewski, Kamil Drejer, Katarzyna Kołodziej +7