Search Results (10)

The high-precision building exterior facade corner point (BEFCP) is an essential element in topographic and cadastral surveys. However, current extraction methods rely on the interactions of humans with the 3D real-scene models produced by unmanned aerial vehicle (UAV) oblique photogrammetry, which have a high workload, low efficiency, poor precision, and cannot satisfy the requirements of automation. The dense point cloud contains discrete 3D building structure information. Still, it is challenging to accurately filter out the partial point cloud characterizing the building structure from it in order to achieve BEFCP extraction. The BEFCPs are always located on the plumb line of the building’s exterior wall. Thus, this paper back-calculated the plumb line from the image and designed a photographic ray corresponding to the image point and point cloud intersection point calculation algorithm to recover its approximate spatial position in order to successfully extract the accurate point cloud in the building structure neighborhood. It then utilized the high signal-to-noise ratio property of the point cloud as a base to eliminate the noise points and, finally, accurately located the building exterior façade corner points by recovering the building structure through segmental linear fitting of the point cloud. The proposed algorithm conducted automated building exterior facade corner point extraction via both of planar-to-stereo and rough-to-precise strategies, reached a 92.06% correctness rate and ±4.5 cm point mean square location error in the experiment, and was able to extract and distinguish the building exterior facade corner points under eaves obstruction and extreme proximity. It is suitable for all high-precision surveying and mapping tasks in building areas based on oblique photogrammetry, which can effectively improve the automation of mapping production. Full article

The consolidation of land to improve the agrarian structure and provide for sustainable rural development is a complex and multi-faceted process, and its efficiency depends on a considerable number of factors associated with its respective stages of desk studies and fieldwork. In order to ensure the highest-quality concepts and their efficient implementation, various measures are undertaken to improve, among other things, the methods for acquiring, collecting, and processing spatial data representing elements of reality saved in cadastral databases. There are a wide variety of available solutions oriented towards land consolidation improvement, but most of them refer to modifications that are difficult to implement due to, for instance, high costs, high technical requirements, and the absence of relevant legal regulations. Our study aimed to find a practical and applicable solution to a material problem in terms of land consolidation projects in Poland, a task associated with the necessity of converting cadastral database objects so that they were suitable for appraising the value of land, and designing new farmsteads based on the value of land held by particular participants of the land consolidation project. It involved the development and implementation of a self-designed algorithm for automated processing of auxiliary land-use/soil-valuation class objects into separate classes representing soil class contours and land use contours, in compliance with the current regulations governing the structure of the cadastre in Poland. The work resulted in the development of an innovative tool, making it possible, among other functions, to align object-generating methods as preferred by the administrator of the cadastral database. The designed algorithm model reduces data processing time to several seconds, while simultaneously eliminating the risk of error. The tool was thoroughly evaluated and then implemented at the Subcarpathian Office of Land Surveying and Agricultural Areas in Rzeszów, which is in charge of land consolidation projects in south-eastern Poland. Full article

Consolidation of land is one of the main procedures for optimising agrarian structures and creating a space for sustainable rural development. A specific feature of a land consolidation project is its complexity and multiple aspects. The considerable number of complex operations making up the whole procedure implies the necessity of using specialist technical tools to ensure effective surveys. The latest developments in technology are giving way to dedicated solutions that can optimise the working time and quality of the outcomes of respective tasks. This paper outlines a self-designed algorithm for generating the technical layer of land use/soil valuation classes based on layers of soil class contours and land use contours, which are obligatory elements of a digital cadastre database in Poland. The fully automated procedure, next to efficient conversion of spatial data, involves detailed verification of the correctness of input data and elimination of various errors. The tool’s deliverable is a set of data necessary for proceeding with selected elements of the land consolidation process, such as comparative estimates of land value and farmstead designs based on land value. The designed tool was implemented at the Subcarpathian Office of Land Surveying and Agricultural Areas in Rzeszów (Poland). The proposed solutions contributed to considerable improvement in the time devoted to performing selected tasks and the substantive correctness of the results. Full article

Estimation of the value of land, underlying the design of constituent plots of the farmstead, is a decisive element of the complex procedure of land consolidation and exchange. Correctly estimated value of agricultural land is a prerequisite for adequate and equitable delimitation of land plots to improve the living conditions of local residents and ensure efficient and profitable agricultural activity. The dynamic development of technology contributes to the development of multiple tools, considerably improving design works and field surveys in the land consolidation process. The world reference literature also gives numerous examples of surveys to optimise the methods for estimating a land value for consolidation projects. However, in our opinion, despite a vast collection of self-designed calculation methods, available sources insufficiently address the optimisation of existing methods based on the current legal framework and implementing practices. This paper presents a self-designed solution for the fully automated performance of complex comparative estimation of land based on the existing cadastral data and a simplified array showing the estimated value. The tool resulted in developing a set of data for directly importing the outcomes of calculations into land surveying software supporting steps of the land consolidation process. Following detailed evaluation, the proposed self-designed solutions were implemented at the Subcarpathian Office of Land Surveying, and Agricultural Areas in Rzeszow for land consolidation works in the Subcarpathian voivodeship in southeastern Poland. Full article

This study develops an automated crop growth detection APP, with the functionality to access the cadastral data for the target field, that was to be used for a satellite-imagery-based field survey. A total of 735 ground-truth records of the cabbage cultivation areas in Yunlin were collected via the implemented APP in order to train a deep learning model to make accurate predictions of the growth stages of the cabbage from 0 to 70 days. A regression analysis was performed by the gradient boosting decision tree (GBDT) technique. The model was trained on multitemporal multispectral satellite images, which were retrieved from the ground-truth data. The experimental results show that the mean average error of the predictions is 8.17 days, and that 75% of the predictions have errors less than 11 days. Moreover, the GBDT algorithm was also adopted for the classification analysis. After planting, the cabbage growth stages can be divided into the cupping, early heading, and mature stages. For each stage, the prediction capture rate is 0.73, 0.51, and 0.74, respectively. If the days of growth of the cabbages are partitioned into two groups, the prediction capture rate for 0–40 days is 0.83, and that for 40–70 days is 0.76. Therefore, by applying appropriate data mining techniques, together with multitemporal multispectral satellite images, the proposed method can predict the growth stages of the cabbage automatically, which can assist the governmental agriculture department to make cabbage yield predictions when creating precautionary measures to deal with the imbalance between production and sales when needed. Full article

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. Full article

Accurate mapping of agricultural fields is needed for many purposes, including irrigation decisions and cadastral management. This paper is concerned with the automated mapping of cropland strips that are common in the North China Plain. These strips are commonly 3–8 m in width and 50–300 m in length, and are separated by small ridges that assist with irrigation. Conventional surveying methods are labor-intensive and time-consuming for this application, and only limited performance is possible with very high resolution satellite images. Small Unmanned Aircraft System (sUAS) images could provide an alternative approach to ridge detection and strip mapping. This paper presents a novel method for detecting cropland strips, utilizing centimeter spatial resolution imagery captured by sUAS flying at low altitude (60 m). Using digital surface models (DSM) and ortho-rectified imagery from sUAS data, this method extracts candidate ridge locations by surface roughness segmentation in combination with geometric constraints. This method then exploits vegetation removal and morphological operations to refine candidate ridge elements, leading to polyline-based representations of cropland strip boundaries. This procedure has been tested using sUAS data from four typical cropland plots located approximately 60 km west of Jinan, China. The plots contained early winter wheat. The results indicated an ability to detect ridges with comparatively high recall and precision (96.8% and 95.4%, respectively). Cropland strips were extracted with over 98.9% agreement relative to ground truth, with kappa coefficients over 97.4%. To our knowledge, this method is the first to attempt cropland strip mapping using centimeter spatial resolution sUAS images. These results have demonstrated that sUAS mapping is a viable approach for data collection to assist in agricultural land management in the North China Plain. Full article

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices. Full article

Cadastres are argued as an essential tool to support land tenure security. Low cadastral coverage in developing countries creates a driver for innovative methods to expedite the mapping processes. As a human construct, the morphology of parcel boundaries is a diverse and complex topic: there are limited generalized rules for identifying, describing, and classifying them. This paper studies both the institutional and spatial aspects of cadastral boundaries, in order to provide more contemporary knowledge about the morphology of cadastral boundaries. This study inspects the relationship between topographic objects and general boundaries in the case context of Port Vila, Vanuatu. Statistical analysis reveals that under a dialectical error tolerance, large percentages of cadastral boundaries coincide with topographic objects. Specifically, in dense urban regions, road edges and building walls coincide with the majority of cadastral boundaries, with proportions of 49% and 35%, respectively. In suburban regions, the fence (25%), instead of buildings, plays an important role in marking a parcel border. The landscape is observed to have significant impact on parcel morphology. Therefore, constructing a map based on automatic or semi-automatic identification and classification of these features could significantly contribute to cadastral mapping in developing countries. Full article

Many developing countries have witnessed the urgent need of accelerating cadastral surveying processes. Previous studies found that large portions of cadastral boundaries coincide with visible physical objects, namely roads, fences, and building walls. This research explores the application of airborne laser scanning (ALS) techniques on cadastral surveys. A semi-automated workflow is developed to extract cadastral boundaries from an ALS point clouds. Firstly, a two-phased workflow was developed that focused on extracting digital representations of physical objects. In the automated extraction phase, after classifying points into semantic components, the outline of planar objects such as building roofs and road surfaces were generated by an α-shape algorithm, whilst the centerlines delineatiation approach was fitted into the lineate object—a fence. Afterwards, the extracted vector lines were edited and refined during the post-refinement phase. Secondly, we quantitatively evaluated the workflow performance by comparing results against an exiting cadastral map as reference. It was found that the workflow achieved promising results: around 80% completeness and 60% correctness on average, although the spatial accuracy is still modest. It is argued that the semi-automated extraction workflow could effectively speed up cadastral surveying, with both human resources and equipment costs being reduced Full article