# MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery

arXiv: https://arxiv.org/abs/1910.12060 · HTML: https://mlsanity.com/paper/1910.12060/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2019.

## Built on by (most cited first, 30 of 35)

- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [Building extraction with vision transformer](https://mlsanity.com/paper/2111.15637/) (2021)
- [Self-Supervised Learning for Invariant Representations from Multi-Spectral and SAR Images](https://mlsanity.com/paper/2205.02049/) (2022)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images](https://mlsanity.com/paper/2102.11262/) (2021)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [TIML: Task-Informed Meta-Learning for Agriculture](https://mlsanity.com/paper/2202.02124/) (2022)
- [HiT: Building Mapping with Hierarchical Transformers](https://mlsanity.com/paper/2309.09643/) (2023)
- [Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training](https://mlsanity.com/paper/2205.08416/) (2022)
- [P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images](https://mlsanity.com/paper/2406.02930/) (2024)
- [Investigating Neural Architectures by Synthetic Dataset Design](https://mlsanity.com/paper/2204.11045/) (2022)
- [PolyBuilding: Polygon Transformer for End-to-End Building Extraction](https://mlsanity.com/paper/2211.01589/) (2022)
- [GraSS: Contrastive Learning with Gradient Guided Sampling Strategy for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2306.15868/) (2023)
- [Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model](https://mlsanity.com/paper/2310.16717/) (2023)
- [Exploiting Semantic Scene Reconstruction for Estimating Building Envelope Characteristics](https://mlsanity.com/paper/2410.22383/) (2024)
- [VectorLLM: Human-like Extraction of Structured Building Contours vis Multimodal LLMs](https://mlsanity.com/paper/2507.04664/) (2025)
- [Building-road Collaborative Extraction from Remotely Sensed Images via Cross-Interaction](https://mlsanity.com/paper/2307.12256/) (2023)
- [DynamicVis: Dynamic Visual Perception for Efficient Remote Sensing Foundation Models](https://mlsanity.com/paper/2503.16426/) (2025)
- [Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility](https://mlsanity.com/paper/2110.04922/) (2021)
- [UAGLNet: Uncertainty-Aggregated Global-Local Fusion Network with Cooperative CNN-Transformer for Building Extraction](https://mlsanity.com/paper/2512.12941/) (2025)
- [A Novel Adaptive Deep Network for Building Footprint Segmentation](https://mlsanity.com/paper/2103.00286/) (2021)
- [Points2Polygons: Context-Based Segmentation from Weak Labels Using Adversarial Networks](https://mlsanity.com/paper/2106.02804/) (2021)
- [MultiScale Probability Map guided Index Pooling with Attention-based learning for Road and Building Segmentation](https://mlsanity.com/paper/2302.09411/) (2023)
- [SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks](https://mlsanity.com/paper/2304.05661/) (2023)
- [Expediting Building Footprint Extraction from High-resolution Remote Sensing Images via progressive lenient supervision](https://mlsanity.com/paper/2307.12220/) (2023)
- [TFNet: Tuning Fork Network with Neighborhood Pixel Aggregation for Improved Building Footprint Extraction](https://mlsanity.com/paper/2311.02617/) (2023)
- [Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction](https://mlsanity.com/paper/2404.10626/) (2024)
- [Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna](https://mlsanity.com/paper/2503.15653/) (2025)
- [MSSFC-Net:Enhancing Building Interpretation with Multi-Scale Spatial-Spectral Feature Collaboration](https://mlsanity.com/paper/2504.00759/) (2025)

## Abstract (arXiv)

Accurately and efficiently extracting building footprints from a wide range of remote sensed imagery remains a challenge due to their complex structure, variety of scales and diverse appearances. Existing convolutional neural network (CNN)-based building extraction methods are complained that they cannot detect the tiny buildings because the spatial information of CNN feature maps are lost during repeated pooling operations of the CNN, and the large buildings still have inaccurate segmentation edges. Moreover, features extracted by a CNN are always partial which restricted by the size of the respective field, and large-scale buildings with low texture are always discontinuous and holey when extracted. This paper proposes a novel multi attending path neural network (MAP-Net) for accurately extracting multiscale building footprints and precise boundaries. MAP-Net learns spatial localization-preserved multiscale features through a multi-parallel path in which each stage is gradually generated to extract high-level semantic features with fixed resolution. Then, an attention module adaptively squeezes channel-wise features from each path for optimization, and a pyramid spatial pooling module captures global dependency for refining discontinuous building footprints. Experimental results show that MAP-Net outperforms state-of-the-art (SOTA) algorithms in boundary localization accuracy as well as continuity of large buildings. Specifically, our method achieved 0.68%, 1.74%, 1.46% precision, and 1.50%, 1.53%, 0.82% IoU score improvement without increasing computational complexity compared with the latest HRNetv2 on the Urban 3D, Deep Globe and WHU datasets, respectively. The TensorFlow implementation is available at https://github.com/lehaifeng/MAPNet.