LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives

2025-03-24

AI Summary

LiDAR remote sensing faces bottlenecks in scalable deployment due to high costs of dense annotation and scarcity of ground-truth observations, resulting in insufficient supervisory signals. To address this, we propose the first unified weakly supervised learning (WSL) theoretical framework and taxonomy specifically designed for LiDAR interpretation and inversion tasks. Our framework systematically integrates three paradigms—incomplete, imprecise, and cross-domain supervision—and introduces LiDAR-specific evaluation dimensions and a technical roadmap tailored to point clouds, waveform data, and rasterized representations. We comprehensively survey 2015–2024 advances, covering multi-instance learning, image-/point-level label learning, self-training, knowledge distillation, domain adaptation, and generative approaches. Our analysis rigorously characterizes applicability boundaries and performance trade-offs across methods, establishing a principled, cost-effective, and scalable methodological foundation for intelligent LiDAR interpretation.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Unsupervised & Self-Supervised Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

LiDAR (Light Detection and Ranging) enables rapid and accurate acquisition of three-dimensional spatial data, widely applied in remote sensing areas such as surface mapping, environmental monitoring, urban modeling, and forestry inventory. LiDAR remote sensing primarily includes data interpretation and LiDAR-based inversion. However, LiDAR interpretation typically relies on dense and precise annotations, which are costly and time-consuming. Similarly, LiDAR inversion depends on scarce supervisory signals and expensive field surveys for annotations. To address this challenge, weakly supervised learning has gained significant attention in recent years, with many methods emerging to tackle LiDAR remote sensing tasks using incomplete, inaccurate, and inexact annotations, as well as annotations from other domains. Existing review articles treat LiDAR interpretation and inversion as separate tasks. This review, for the first time, adopts a unified weakly supervised learning perspective to systematically examine research on both LiDAR interpretation and inversion. We summarize the latest advancements, provide a comprehensive review of the development and application of weakly supervised techniques in LiDAR remote sensing, and discuss potential future research directions in this field.

Problem

• Reducing costly dense annotations for LiDAR interpretation
• Addressing scarce supervisory signals in LiDAR inversion
• Unifying weakly supervised learning for LiDAR tasks

Innovation

• Weakly supervised learning for LiDAR interpretation
• Unified approach for LiDAR inversion and interpretation
• Reducing annotation costs with weak supervision