This article discusses the use of Artificial Intelligence (AI) to classify cocoa beans as healthy or diseased based on established classification criteria, given the challenges faced by the cocoa industry due to the impact of diseased beans on quality and grading. The proposed method uses YOLOv5 and achieved an 94.5% accuracy rate. The article also outlines the development of an affordable and easy-to-implement prototype system that cocoa farmers can use to grade and assure bean quality. The results suggest that the proposed system is successful, and increasing the amount of data improves its reliability, which could help farmers improve their competitiveness in the market.

Estimating unknown values using its surrounding measured values is called spatial interpolation, a vital tool for estimating continuous spatial data such as the earth's surface. Construction of the Digital Elevation Model is one of the most common applications of spatial interpolation methods. There are various global and local interpolation techniques, including Kriging, Inverse Distance Weighted (IDW), Thiessen polygons (TIN), Natural Neighbor (NN), and Spline interpolation. This paper introduces the interval-valued homotopy continuation for 3D spatial data interpolation. Straight lines or algebraic curves can be reconstructed using homotopy continuation between any pairs of 3D data. The novel method of the interval-valued homotopy to restore the topographic surface between spatial data is developed in MATLAB programming language. For a dataset of ASTER GDEM, the presented mathematical algorithm shows better results compared to TIN and IDW methods in terms of Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error with values of 5.2897, 1.53, and 2.299 m, respectively.

In spatial science, the relationship between spatial objects is considered to be a vital element. Currently, 3D objects are often used for visual aids, improving human insight, spatial observations, and spatial planning. This scenario involves 3D geometrical data handling without the need for topological information. Nevertheless, in the near future, users will shift to more complex queries corresponding to the existing 2D spatial approaches. Therefore, having 3D spatial objects without having these relationships or topology is impractical for 3D spatial analysis queries. In this paper, we present a new method for creating topological information that we call the Compact Abstract Cell Complexes (CACC) data structure for 3D spatial objects. The idea is to express in the most compact way the topology of a model in 3D (or more generally in nD) without requiring the topological space to be discrete or geometric. This is achieved by storing all the atomic cycles through the models (null combinatorial homotopy classes). The main idea here is to store the atomic paths through the models as an ant experiences topology: each time the ant perceives a previous trace of pheromone, it knows it has completed a cycle. The main advantage of this combinatorial topological data structure over abstract simplicial complexes is that the storage size of the abstract cell cycles required to represent the geometric topology of a model is far lower than that for any of the existing topological data structures (including abstract simplicial cell cycles) required to represent the geometric decomposition of the same model into abstract simplicial cells. We provide a thorough comparative analysis of the storage sizes for the different topological data structures to sustain this.

Drones are becoming popular in spatial mapping or survey. The use of drones survey can be seen from it low flying heights (capable to create a clear images), accessible on difficult or non-friendly vehicle access areas, faster data acquisition and higher data resolution henceforth improve the quality of the survey. However, this paper focuses on the post-processing of drone images for 3D surface modeling. With the motivation of producing better 3D models, four software packages are used for comparison. Those software packages are eyesMap3D, Drone Deploy, Agisoft PhotoScan and Pix4Dmapper. The equipment used to ensure a high level of quality model is the Leica GPS1200+ stationary GPS module and the DJI Phantom 4 PRO drone. The Leica GPS1200+ stationary GPS module were used to track the exact position of tie points on the ground. Meanwhile the DJI Phantom 4 PRO drone is used as data inputs (images) for the software packages stated. In addition, the drone is used to fly over a golf course, with a challenge of homogenous surface for 3D surface modeling. Based on the output, it shows that each software packages produces slightly different outputs. This paper summarizes the outputs and discusses the key elements in each software packages. This variation might be useful for future references in 3D surface modeling that can conform in different applications requirements.

The present study investigates the use of close-range photogrammetry for automated air void analysis of hardened concrete without contrast enhancement.First, polished plane sections of concrete with varying air contents were analyzed by photogrammetry. The data in the form of point clouds were processed to identify the air voids. Second, the same plane sections were subjected to traditional air void analysis by contrast enhancement. Results of both methods were compared.The method based on photogrammetry showed potential, but it could not capture the smallest air voids. Better camera resolution and/or methods to reduce noise in the point cloud are needed.

In this article, several mathematical and statistical methods for uncertainty modelling in indoor buildings are investigated. To reduce the time and cost of the indoor building data-acquisition process, the Trimble LaserAce 1000 rangefinder is used. The accuracy of the rangefinder is evaluated and a simple spatial model is reconstructed. The rangefinder has been used for forest applications to measure the height of trees. In this article, the rangefinder is used for indoor building environment data collection and mapping. The rangefinder was calibrated using a least squares adjustment algorithm and a novel method of interval-valued homotopy as a continuous deformation that reconstructs straight lines or algebraic curves between any pair of three-dimensional data. Homotopy gives better results in term of root mean square error than conventional surveying engineering methods where, in some cases, cubic homotopy gives results 11 times better than conventional methods.

Crisp clustering is a technique to cluster objects into group without having overlapping partitions. Each data point is either belongs to or not to a group. Most of the clustering algorithms are categorized as crisp clustering. There are several categories of crisp clustering algorithm such as partitional algorithm, hierarchical algorithm, density-based algorithm, and grid-based algorithm. The general definition of each group could be defined as follows (Kovács et al. 2005):

Crisp clustering is a technique to cluster objects into group without having overlapping partitions. Each data point is either belongs to or not to a group. Most of the clustering algorithms are categorized as crisp clustering. There are several categories of crisp clustering algorithm such as partitional algorithm, hierarchical algorithm, densitybased algorithm, and grid-based algorithm. The general definition of each group could be defined as follows (Kovács et al. 2005): • Partitional algorithms: divide the data into a set of separate category. This algorithm attempts to define the number of partitions to optimize a certain criterion function. This optimization is an iterative procedure. • Hierarchical algorithms: This algorithm creates clusters repeatedly by merging a small cluster into a larger cluster. It also split cluster into several small classes. • Density-based algorithms: By using this technique, clusters are generated based on its density function and produced arbitrary shaped clusters. • Grid-based algorithms: These types of algorithms are widely used for the application of spatial data mining. The search space is quantized into a finite number of cells.

Abstract. Indoor surveying is currently based on laser scanning technology, which is time-consuming and costly. A construction model depends on complex calculations which need to manage a large number of measured points. This is suitable for the detailed geometrical models utilized for representation, yet excessively overstated when a simple model including walls, floors, roofs, entryways, and windows is required, such a basic model being a key for efficient network analysis such as shortest path finding. To reduce the time and cost of the indoor building data acquisition process, the Trimble LaserAce 1000 range finder is used. A comparison of neural network and a combined method of interval analysis and homotopy continuation in 3D interior building modelling for calibration of inaccurate surveying equipment is presented. We will present the interval valued homotopy model of the measurement of horizontal angles by the magnetometer component of the rangefinder. This model blends interval analysis and homotopy continuation. The results prove that homotopies give the best results both in terms of RMSE and the L∞ metric.

This book presents the latest research developments in geoinformation science, including such sub-disciplines of the field as geomatic engineering, GIS, remote sensing, digital photogrammetry, digital cartography and more.

Nowadays, municipalities intend to have 3D city models for facility management, disaster management and architectural planning. Indoor models can be reconstructed from construction plans but sometimes, they are not available or very often, they differ from ‘as-built’ plans. In this case, the buildings and their rooms must be surveyed. One of the most utilized methods of indoor surveying is laser scanning. The laser scanning method allows taking accurate and detailed measurements. However, Terrestrial Laser Scanner is costly and time consuming. In this paper, several techniques for indoor 3D building data acquisition have been investigated. For reducing the time and cost of indoor building data acquisition process, the Trimble LaserAce 1000 range finder is used. The proposed approache use relatively cheap equipment: a light Laser Rangefinder which appear to be feasible, but it needs to be tested to see if the observation accuracy is sufficient for the 3D building modelling. The accuracy of the rangefinder is evaluated and a simple spatial model is reconstructed from real data. This technique is rapid (it requires a shorter time as compared to others), but the results show inconsistencies in horizontal angles for short distances in indoor environments. The range finder horizontal angle sensor was calibrated using a least square adjustment algorithm, a polynomial kernel, interval analysis and homotopy continuation.

An algorithm for image matching of multi-sensor and multi-temporal satellite images is developed. The method is based on the SIFT feature detector proposed by Lowe in (Lowe, 1999). First, SIFT feature points are detected independently in two images (reference and sensed image). The features detected are invariant to image rotations, translations, scaling and also to changes in illumination, brightness and 3-dimensional viewpoint. Afterwards, each feature of the reference image is matched with one in the sensed image if, and only if, the distance between them multiplied by a threshold is shorter than the distances between the point and all the other points in the sensed image. Then, the matched features are used to compute the parameters of the homography that transforms the coordinate system of the sensed image to the coordinate system of the reference image. The Delaunay triangulations of each feature set for each image are computed. The isomorphism of the Delaunay triangulations is determined to guarantee the quality of the image matching. The algorithm is implemented in Matlab and tested on World-View 2, SPOT6 and TerraSAR-X image patches.

The emergence of new digital aerial cameras and the diverse design and technology used in this type of cameras require in-situ calibration. Self-calibration methods, e.g. the Fourier model, are primarily used; however, additional parameters employed in such methods have not yet met the expectations to desirably model the complex multiple distortions existing in the digital aerial cameras. The present study proposes the Chebyshev–Fourier (CHF) and Jacobi–Fourier (JF) combined orthogonal models. The models are evaluated for the multiple distortions using both simulated and real data, the latter being derived from an UltraCam digital camera. The results indicate that the JF model is superior to the other methods where, e.g., in the UltraCam scenario, it improves the planimetric and vertical accuracy over the Fourier model by 18% and 22%, respectively. Furthermore, a 30% and 16% of reduction in external and internal correlation is obtained via this approach which is very promising.

Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usually large due to its detail geometry and other attached information. In this research, a clustered 3D hierarchical structure is introduced as a 3D spatial access method structure. The structure is expected to improve the retrieval of Nearest Neighbour information for 3D objects. Several tests are performed in answering Single Nearest Neighbour search and k Nearest Neighbour (kNN) search. The tests indicate that clustered hierarchical structure is efficient in handling Nearest Neighbour query compared to its competitor. From the results, clustered hierarchical structure reduced the repetitive data entry and the accessed page. The proposed structure also produced minimal Input/Output operation. The query response time is also outperformed compared to the other competitor. For future outlook of this research several possible applications are discussed and summarized.

Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Keywords: Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review

The growth of urban areas has resulted in massive urban datasets and difficulties handling and managing issues related to urban areas. Huge and massive datasets can degrade data retrieval and information analysis performance. In addition, the urban environment is very difficult to manage because it involves various types of data, such as multiple types of zoning themes in the case of urban mixed-use development. Thus, a special technique for efficient handling and management of urban data is necessary. This paper proposes a structure called Classified and Clustered Data Constellation (CCDC) for urban data management. CCDC operates on the basis of two filters: classification and clustering. To boost up the performance of information retrieval, CCDC offers a minimal percentage of overlap among nodes and coverage area to avoid repetitive data entry and multipath query. The results of tests conducted on several urban mixed-use development datasets using CCDC verify that it efficiently retrieves their semantic and spatial information. Further, comparisons conducted between CCDC and existing clustering and data constellation techniques, from the aspect of preservation of minimal overlap and coverage, confirm that the proposed structure is capable of preserving the minimum overlap and coverage area among nodes. Our overall results indicate that CCDC is efficient in handling and managing urban data, especially urban mixed-use development applications.

Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.