SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes

2026-04-23

AI Summary

This work addresses the challenges of ground filtering in airborne laser scanning (ALS) point clouds, particularly the trade-off between contextual awareness and fine-detail preservation, as well as the misclassification of tall objects in cross-urban and natural scenes. To this end, we propose a sparse segmentation framework that integrates context compression with height-aware modeling. Our approach employs a convex-mirror-inspired context compression module to retain local geometric details while aggregating large-scale contextual information. This is combined with a sparse voxel-point hybrid network architecture and a height-aware loss function that explicitly incorporates terrain elevation priors. Evaluated on two major ALS benchmark datasets, the method achieves state-of-the-art performance in complex urban environments, demonstrates competitive results across mixed terrains, and maintains robust accuracy in densely vegetated, steep forested areas.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Machine Learning: Active Learning

Application Category

• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

High-quality digital terrain models derived from airborne laser scanning (ALS) data are essential for a wide range of geospatial analyses, and their generation typically relies on robust ground filtering (GF) to separate point clouds across diverse landscapes into ground and non-ground parts. Although current deep-learning-based GF methods have demonstrated impressive performance, especially in specific challenging terrains, their cross-scene generalization remains limited by two persistent issues: the context-detail dilemma in large-scale processing due to limited computational resources, and the random misclassification of tall objects arising from classification-only optimization. To overcome these limitations, we propose SparseGF, a height-aware sparse segmentation framework enhanced with context compression. It is built upon three key innovations: (1) a convex-mirror-inspired context compression module that condenses expansive contexts into compact representations while preserving central details; (2) a hybrid sparse voxel-point network architecture that effectively interprets compressed representations while mitigating compression-induced geometric distortion; and (3) a height-aware loss function that explicitly enforces topographic elevation priors during training to suppress random misclassification of tall objects. Extensive evaluations on two large-scale ALS benchmark datasets demonstrate that SparseGF delivers robust GF across urban to natural terrains, achieving leading performance in complex urban scenes, competitive results on mixed terrains, and moderate yet non-catastrophic accuracy in densely forested steep areas. This work offers new insights into deep-learning-based GF research and encourages further exploration toward truly cross-scene generalization for large-scale environmental monitoring.

Problem

• ground filtering
• cross-scene generalization
• context-detail dilemma
• misclassification of tall objects
• airborne laser scanning

Innovation

• context compression
• height-aware loss
• sparse segmentation
• ground filtering
• cross-scene generalization