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.
Abstract (translated)
LiDAR（激光探测与测距）技术能够快速且精确地获取三维空间数据，在遥感领域，如地形测绘、环境监测、城市建模和森林资源调查等方面得到广泛应用。LiDAR遥感主要包括数据解释和基于LiDAR的反演两个方面。然而，传统的LiDAR数据解释通常依赖于密集而精准的手动标注，这既耗时又成本高昂。同时，基于LiDAR的反演也需要稀缺且昂贵的现场调查来获取必要的监督信号。 为了解决这些问题，在过去几年中，弱监督学习方法得到了广泛的关注，并且出现了许多使用不完整、不准确或来自其他领域的注释数据的方法，以解决LiDAR遥感任务。现有的综述文章通常将LiDAR解释和反演视为两个独立的任务进行讨论。本文首次从统一的弱监督学习视角出发，系统地考察了有关这两个方面的研究工作。我们总结最新的进展，并全面回顾在LiDAR遥感中应用弱监督技术的发展与应用情况，同时探讨该领域的潜在未来研究方向。
URL
https://arxiv.org/abs/2503.18384
