Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

Abstract

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02-0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

Bridging the Semantic Gap between Land Tenure and EO Data: Conceptual and Methodological Underpinnings for a Geospatially Informed Analysis

“…In light of state-of-the-art methods in land administration, a deep-learning is becoming highly prominent for the detection of cadastral boundaries [12,19]. Recent evidence suggests that deep fully convolutional networks (FCNs) ensures the high accuracy rather than gPb and MRS, with results of 0.79 in precision, 0.37 in recall and 0.50 in F-score [19].…”

“…Recent evidence suggests that deep fully convolutional networks (FCNs) ensures the high accuracy rather than gPb and MRS, with results of 0.79 in precision, 0.37 in recall and 0.50 in F-score [19]. For optimizing image segmentation, one study by [12] not only introduced the interactive boundary delineation workflow, but also examined the better suitability of the deep learning in cadastral mapping with convolutional neural networks (CNNs) by comparing random forest (RF) in machine learning: RF-derived boundary likelihoods (accuracy: 41%, precision: 49%), CNN-derived boundary likelihoods (accuracy: 52%, precision: 76%).…”

“…Convolutional Networks (FCNs) [19] Aerial imagery and UAVs √ Automatic boundary classification: Random Forest (RF), Convolutional Neural Networks (CNN) [12] Remote Sens. 2020, 12, 255 6 of 26…”

“…Literature shows applications ranging from environmental and regional studies to economics, and peace and conflict research, for example [6][7][8][9]. More specific to the interest of this paper, there is growing body of literature on methods to extract and map cadastral boundaries using EO data [10][11][12][13][14][15][16][17][18][19]. However, this literature rarely effectively bridges the knowledge gap between social land tenure and spatial descriptions of boundaries.…”

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

“…These networks have the following characteristics: they are fully convolutional networks without fully connection; a skip connection structure combined with deconvolution layers and convolution layers at different depths so as to revert the accurate locations of the geographic objects and add semantic labels to each pixel of the image. The semantic segmentation networks based on CNNs are widely applied in the recognition of buildings [38][39][40][41], the extraction of cadastral boundaries [42] and the land use or land cover change [43,44]. The applications are also expanded to the recognition of the agricultural plants [45], pests and diseases [46,47], especially the Refs.…”