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Despite substantial technological advancements, the comprehensive mapping ofsurface water, particularly smaller bodies (<1ha), continues to be a challengedue to a lack of robust, scalable methods. Standard methods require eithertraining labels or site-specific parameter tuning, which complicates automatedmapping and introduces biases related to training data and parameters. Thereliance on water's reflectance properties, including LiDAR intensity, furthercomplicates the matter, as higher-resolution images inherently produce morenoise. To mitigate these difficulties, we propose a unique method that focuseson the geometric characteristics of water instead of its variable reflectanceproperties. Unlike preceding approaches, our approach relies entirely on 3Dcoordinate observations from airborne LiDAR data, taking advantage of theprinciple that connected surface water remains flat due to gravity. Byharnessing this natural law in conjunction with connectivity, our method canaccurately and scalably identify small water bodies, eliminating the need fortraining labels or repetitive parameter tuning. Consequently, our approachenables the creation of comprehensive 3D topographic maps that include bothwater and terrain, all performed in an unsupervised manner using only airbornelaser scanning data, potentially enhancing the process of generating reliable3D topographic maps. We validated our method across extensive and diverselandscapes, while comparing it to highly competitive Normalized DifferenceWater Index (NDWI)-based methods and assessing it using a reference surfacewater map. In conclusion, our method offers a new approach to addresspersistent difficulties in robust, scalable surface water mapping and 3Dtopographic mapping, using solely airborne LiDAR data.

Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data