Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules’ tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%.

Learning and implementing algorithms is a fundamental but challenging aspect of Computer Science education. One of the key tools used in teaching algorithms is pseudocode, which serves as an abstract representation of the logic behind a given algorithm. This study explores the educational value of the FLoCIC (Few Lines of Code for Image Compression) algorithm, which is designed to teach lossless image compression through algorithmic implementation, particularly within the context of multimedia data. Image compression represents a typical multimedia task that combines algorithmic thinking with practical problem-solving. By analysing questionnaire responses (N = 121) from undergraduate and graduate students, this study identifies critical challenges in pseudocode-based learning, including understanding complex algorithmic components and debugging recursive functions. This paper highlights the influence of prior knowledge in areas such as data structures, compression, and algorithms in general on the success of students in completing the task, with graduate students demonstrating stronger results compared to undergraduates. The study analyses the role of external resources and online code repositories, further revealing their utility in supporting implementation efforts but highlighting the need for a fundamental understanding of the algorithm for successful implementation. The findings highlight the importance of promoting conceptual understanding and practical problem-solving skills to improve student learning in algorithmic tasks.

A new compact encoding is presented of rasterized bi-level shapes at multiple resolutions. The encoder accepts the Freeman chain code in four directions (F4) at the input, and builds a multi-resolution code named MrCC. The encoding process constructs the coarser representation of F4 chain code, and the resulting MrCC codes simultaneously. MrCC encodes the differences between sequences of F4 chain code from successive resolutions. Several transformations are performed during this process. Various rasterized shapes were used to analyse the efficiency of the new code, which was, on average, 22% better than the concatenation of F4 chain codes of various resolutions.

Reliable forest growth forecasting requires detailed tree data for forest simulation, while manual on-site collection of relevant data is work-intensive and unfeasible in larger forests. This paper proposes a complete methodology for fully automated forest growth simulation that relies primarily on airborne topographic Light Detection And Ranging (LiDAR) point clouds of individual trees. The proposed method estimates tree parameters and performs growth of individual trees based on an individual-based forest growth simulator, named BWINPro. In addition, competition and detailed asymmetric tree crown growth are modeled regarding the shading of tree crowns, which is estimated from the surrounding environment and neighbor trees. The result of the proposed approach is a new point cloud for subsequent analyses. The proposed method was validated by comparing canopy height models derived from the point clouds of the simulated trees with canopy height models derived from more recent ground truth point clouds. The results demonstrate the efficacy of the proposed method which achieves a 9.4% higher accuracy than the averaged linear regression model and, in the case of datasets with more distinct self-standing trees, where a tree crown boundary plays major role, a 4.1% higher accuracy than the directly fitted linear regression model.

Trees are natural objects, where deviations through the branches amplify geometric data for 3D representation and bring challenges to various applications dealing with 3D models, such as compression, visualization, symmetry detection, and radiative transfer simulation. This data article describes dataset of approximately symmetric 3D tree models with manually identified predominant symmetry plane in each tree model. Parameters for procedural tree synthesis were manually adjusted to produce approximately bilaterally symmetric trees which are grouped into species with distinct features. In the last step, each tree was manually annotated with approximate symmetry plane. This dataset contains geometric data of branches, manually defined parameters for tree synthesis method, point clouds, and a division plane with a score of bilateral symmetry strength. The generated trees can be used as benchmark data for verification of approximate reflectional symmetry detection methods. Additionally, generated 3D tree models can be used for other applications requiring pregenerated trees, such as compression of tree models, instancing, decimation methods, and radiative transfer simulation and modeling.

This paper introduces a new lossy approach for compression of cartoon images. The image is firstly partitioned into regions of roughly the same colour. The chain codes are then determined of all regions. The sequence of the obtained chain code symbols is transformed with the Burrows-Wheeler Transform, Move-To-Front transform, and compressed with Run-Length Encoding. In the final step, an arithmetic encoder may be used to compress the obtained binary stream additionally. The proposed algorithm is asymmetric, meaning that the decompression does not reverse all the steps of the compression procedure. The experimental results have shown that the described method produces considerably better compression ratios than JPEG, JPEG2000, WebP, SPIHT, PNG, and two of the algorithms specialised in compression of cartoon images: the algorithm using quad-tree, and RS-LZ algorithm.

With the growing urbanization and environmental concerns over buildings' energy consumption and carbon footprint, the demand for energy-efficient building design is greater than ever. This paper addresses these concerns by presenting a novel method for estimating And optimising the thermal load (i.e. total energy load for heating and cooling) of a building within a real environment, provided by high-resolution LiDAR data, while considering long-term climatological parameters, estimated direct and anisotropic diffuse irradiance, shadowing from surroundings, and terrain topography. In the optimisation part of the method, the building's design is optimised regarding the estimated thermal load. The estimation was validated with the well-established EnergyPlus software. In experiments, a rectangular building's design was optimised on a flat and urban dataset. The effect of a building's design parameters on thermal load Was inspected as Well. On average, the proposed method improved a building's net heat gain by over 103 kWh/m(2) and reduced its thermal load by 234.18 kWh/m(2) when compared with the initial building design.

Competition between individual trees is a major factor influencing the development of forests. However, due to the complexity of such interactions, that span over vast geographic areas, systematic analysis of competition has only recently become possible through the concepts of so-called predictive analytics. The rationale behind the utilised approach is that a prediction model, which is capable of forecasting future increments of tree development parameters accurately, contains knowledge about the underlying relationships that govern them. The analysis of such model, therefore, holds the potential to reveal new insights into the critical factors that influence forest developments. Within this study, we utilise an Evolutionary Algorithm in order to enable predictive analytics based on a complex-network representation of competition. This allowed us to study the patterns related to spatial distribution of individual trees. We discovered that triplets of competing trees, and their betweenness centralities, have significantly greater influence on the development of each individual tree than traditionally observed parameters like the number of a tree's competitors and distances between them. While this indicates preferable spatial patterns for optimal forest development, the introduced methodology proved to be an efficient predictive analytics tool that allows for their discovery.

This paper considers a new contour-based representation of binary objects in raster images. Low processing and storage requirements of the decoder, satisfactory compression ratio and generality make this chain coding technique interesting for storing predefined graphical objects in embedded systems. Three improvements of the DDCC code were introduced. Extra Huffman codes are assigned to two frequent pairs of symbols, 135° directional differences in concave angles are omitted since they do not affect the outer object shape and, finally, longer line segments are run-length encoded. Comparison with six other chain coding techniques of similar implementation complexity confirms that the new technique represents an efficient alternative way to encode 8-connected contours.

A pseudo-triangulation is a planar subdivision into polygons with three convex vertices, useful for ray shooting, visibility problems and kinetic collision detection. As pseudo-triangulations are quite young, there is a lack of specialized algorithms for them. In this paper, we address the question of location in pseudo-triangulations. We propose two location algorithms based on the so-called stochastic walk and present their experimental results. The class of walk location algorithms is very popular for triangulations, namely in engineering applications, due to simplicity and low memory requirements, in spite of their non-optimality. As far as we know, no walk algorithm specialized on pseudo-triangulations has been developed before.

Light Detection and Ranging (LIDAR) has become one of the prime technologies for rapid collection of vast spatial data, usually stored in a LAS file format (LIDAR data exchange format standard). In this article, a new method for lossless LIDAR LAS file compression is presented. The method applies three consequent steps: a predictive coding, a variable-length coding and an arithmetic coding. The key to the method is the prediction schema, where four different predictors are used: three predictors for x, y and z coordinates and a predictor for scalar values, associated with each LIDAR point. The method has been compared with the popular general-purpose methods and with a method developed specially for compressing LAS files. The proposed method turns out to be the most efficient in all test cases. On average, the LAS file is losslessly compressed to 12% of its original size.

This paper presents a new lossless raster font compression method that uses vertex chain code to define character's outline. Obtained chain codes are compressed by Huffman coding algorithm. The results show that the new method requires least memory space to store the raster fonts among the known methods. Moreover, the font size has almost no impact on the coder efficiency. Due to the low complexity of the decoder that occupies only 2.7kB of memory space, this method is ideal for use in embedded systems.

Remote sensing technologies, such as LIDAR, rapidly evolve and produce large datasets. The computers used to visualize these data have limited resources, which prevent detailed and real-time visualization. An approach to real-time visualization of virtually unlimited LIDAR datasets, at full detail with a hierarchical and out-of-core approach to data management and a modern point-based rendering technique, is presented. It is based on on-demand loading of data subsets into their optimal memory locations and on a two-pass point-based rendering method that utilizes elliptical weighted average filtering to reduce alias. In addition, points are rotated in accordance to their distance and orientation from the viewer. All graphics computations are implemented in programmable shaders on the GPU, so the CPU is free to perform other tasks. The capability of our approach is compared with the Kreylos et al. (2008) method.

This paper introduces a new algorithm for the compression of manifold hexahedral meshes topology, using vertex degree. The topology compression consists of two parts—the mesh’s boundary consisting of quadrilaterals is compressed first, and then the hexahedra are processed by the help of six commands. The topology compression algorithm has been matched against the best-known method to-date, and shows itself to be competitive.

General constructive geometric constraint solvers are pre-processed by a degree-of-freedom analysis, which enables efficient graph decomposition and recombination. However, all these methods are based on the assumption that structural rigidity automatically assures solvability. In this paper, we show that this assumption fails in numerous, even the most basic, configurations. We introduce several simple but efficient rules aimed to additionally analyse solvability in such cases. Another novelty addresses conditional constraints between three or more geometric parts, rules for their simplification and a redundancy check. All these functionalities are built into our original 2D geometric constraint solver, based on concepts of rigid clusters and constrained-angle (CA) sets.

This paper introduces a universal algorithm for polygon clipping, which is a frequent operation in GIS. In the proposed solution, the clipping polygons can be concave and may include holes. This algorithm is based on so-called entry/exit intersection point property, which has to be explicitly determined only at the first calculated intersection point. It uses a simple but efficient data structure based on a single-linked list. Boolean union and the difference between input polygons can also be determined after small modifications. This algorithm can easily be adapted to Boolean operations between regions composed of polygon sets.

Virtual Prototypes (VPs) are important and useful tools for engineers providing the capability to evaluate designs under 'real-world' conditions during different phases of their development. The goal of research is to create and implement a computer model to: (1) handle viewing and interacting of VPs over the web, (2) enable exchanging configuration of the VPs and manipulate appearances of parts, and (3) enable the propagation of the chosen configuration directly to the modelling system. The aim is avoiding repetition of VPs preparation for various configurations and enables cooperation of a broad set of participants regardless of their computer experience.

Given an input set of planar points, which occupy a non-convex polygon area, possibly with holes, we reconstruct the shape of its boundary domain, without previous knowledge of which points or edges belong to the boundary. Our approach is based on different qualities of the Delaunay triangles inside and outside the domain. This method is heuristic and does not ensure success in all cases but it is very simple and there is no other method for this problem known to us. The method was derived on real GIS data but experiments show that it could also be used for mechanical engineering data, with positive results.

The paper introduces a new method for lossless compression of triangular meshes. The number of commands describing the spatial relationships of the mesh is reduced by expressing the topological relation of pair of triangles. The approach bases on the eight states, which are described by the four commands. A comparison with Touma and Gotsman approach has been made using over 150 testing examples. The proposed approach turns out as more suitable for 2.5D and regular 3D triangular meshes.