Solving the horizontal conflation problem with a constrained Delaunay triangulation

Abstract

Datasets produced by different countries or organisations are seldom properly aligned and contain several discrepancies (e.g., gaps and overlaps). This problem has been so far almost exclusively tackled by snapping vertices based on a user-defined threshold. However, as we argue in this paper, this leads to invalid geometries, is error-prone, and leaves several discrepancies along the boundaries. We propose a novel algorithm to align the boundaries of adjacent datasets. It is based on a constrained Delaunay triangulation to identify and eliminate the discrepancies, and the alignment is performed without moving vertices with a snapping operator. This allows us to guarantee that the datasets have been properly conflated and that the polygons are geometrically valid. We present our algorithm, our implementation (based on the stable and fast triangulator in CGAL), and we show how it can be used it practice with different experiments with real-world datasets. Our experiments demonstrate that our approach is highly efficient and that it yields better results than snapping-based methods.

Multisource spatial data integration for use cases applications

“…Data fusion is a common process for remote sensing applications, and is usually performed by spatial statistics applications (Ghamisi et al, 2019; Nguyen et al, 2012; Zhang, 2010). Other data fusion examples exist for 2D maps, mainly for data update goals (Chen et al, 2013; Devogele, 2002; Duckham & Worboys, 2005) or for harmonizing cross‐border maps (Ledoux & Arroyo Ohori, 2017; Noardo et al, 2016). Furthermore, other studies consider the fusion of complex information and multi‐sensor 3D data (Ahn et al, 2020; Dong et al, 2018; Ramos & Remondino, 2015; Wallgrün & Dylla, 2010; Zhu & Donia, 2013).…”

Multisource Spatial Data Integration for Use Cases Applications

“…Such priorities should be ( 1 Data fusion is a common process for remote sensing applications, and is usually performed by spatial statistics applications (Nguyen et al, 2012;Zhang, 2010;Ghamisi et al, 2019). Other data fusion examples exist for 2D maps, mainly for data update goals (Duckham, Worboys, 2005;Chen et al, 2013;Devogele, 2002) or for harmonising cross-border maps (Ledoux, Arroyo Ohori, 2017;Noardo et al, 2016).…”

Multisource Spatial Data Integration for Use Cases Applications

“…Data fusion is a common process for remote sensing applications, and is usually performed by spatial statistics applications (Nguyen et al, 2012;Zhang, 2010;Ghamisi et al, 2019). Other data fusion examples exist for 2D maps, mainly for update goals (Duckham, Worboys, 2005;Chen et al, 2013;Devogele, 2002) or for harmonising cross-border maps (Ledoux, Arroyo Ohori, 2017;Noardo et al, 2016). Furthermore, more recent studies take into account more complex information and multi-sensor 3D data (Wallgrün, Dylla, 2010;Dong et al, 2018;Ramos, Remondino, 2015;Zhu, Donia, 2013;Ahn et al, 2020).…”