Enhancing the resolution of point clouds is crucial in achieving detailed and precise 3D representations for various applications. Factors such as sensor calibration, scanning range, and environmental capability play a pivotal role in determining the overall quality of the captured point cloud data. Moreover, issues related to noise, occlusions, and sensor limitations can further impact the accuracy of the modelling outcome, underscoring the importance of optimizing point cloud resolution. Thus, researchers started to build new architectures with the aim of produce more dense and complete representation with higher resolution. Different methods have been created to achieve successful upsampling, such as interpolation techniques, deep learning strategies, and optimization algorithms. In this paper, we take a closer look at this exceptionally fast-developing field of science. According to this aim, the reader will better understand point cloud upsampling technology.

Geospatial data with high resolution and spatio-temporal accuracy can further support sustainable infrastructure and optimise urban services to improve the quality of life of city residents. LiDAR-based technologies are commonly used to produce 3D urban models and can include terrestrial laser scanning (TLS), mobile mapping systems (MMS), and airborne platforms such as photogrammetric drones. Point cloud datasets can be utilised for transportation planning and management, utility management, green infrastructure evaluation, and emergency response. Despite the utility of these point cloud datasets, the intrinsic incompleteness or sparsity due to the costs of surveying, the characteristics of the sensors, and environmental occlusion are significant limitations for effective precision modelling at the urban scale. Point cloud upsampling appears to be an innovative modelling gap for synthetically increasing point density, while preserving geometric accuracy. Deep learning–based networks demonstrably reduced the quantified improvements of the point cloud upsampling method. Previous studies have shown that reduced point-to-surface deviation from ~0.146 to ~0.140 (10^-2 scale; 6.11 % improvement), and improved distribution uniformity from 0.315 to 0.219 (30.55 % improvement), and frequency-selective geometry upsampling provided up to 4.4×s less point-to-point compared to PU-Net and at 4× upsampling factors These results demonstrate that advanced point cloud upsampling methods would reasonable improve the accuracy or precision of derived products such as digital terrain models (DTMs), canopy height models (CHMs), and other ecological indices that are generally sensitive to point density. This paper reviews the latest upsampling algorithms and proposes a way of thinking and structuring data science that can scale into urban monitoring processes.

High-resolution, dynamic geospatial data support sustainable infrastructure, optimize urban services, and improve the quality of life of residents. Airborne Laser Scanning is an increasingly popular remote sensing technology that can be used to collect very large datasets of 3D point clouds over extensive areas, including forests, river basins, coastal wetlands, and mountainous regions. These datasets facilitate the analysis of vegetation structure, biomass estimation, hydrological modeling, and land cover detection or change monitoring. However, Airborne Laser Scanning-derived point clouds are typically limited to low density and spatial resolution, which preclude meaningful analysis for fine-scale ecological and environmental modeling. Point cloud upsampling is a permissible way to augment the spatial robustness of Airborne Laser Scanning point cloud data, and does so without adding a logistical burden of the data acquisition in the field, or the need to resurvey at high costs and time. Upsampling is synthetic in nature, achieving increased data point count, but maintaining dimensional integrity for continuity of surfaces and geometric fidelity, which is essential in methodologies that intervene for derived products such as digital terrain models, canopy height models, and vegetation metrics. This manuscript examines using point cloud upsampling as part of environmental monitoring. It reviews the upsampling algorithms that have been developed to date, synthesizes existing methods, and considers their relevance to the state of practice in forestry, watershed management, and conservation planning. The work considers and focuses on methodological bases for robustness and dimensionality, and although considerably nuanced, the methodological efficacy is subtended and suggests how enhanced points improve outcomes for ecological models and the information provided supports resource management decisions related to resource and sustainability decisions.. Ultimately and conclusively, the work establishes the understanding of point cloud enhancement for its visualization, but also its potential as an emergent action that contributes to construction and promotes a sustainability intention in environmental science and policy. This article appears as a mini-review. This writing aims to synthesise existing knowledge and conceptual strategies, rather than a novel outcome of an experiment. It is written to give an overview of existing methods and structured conceptual frameworks for employing point cloud upsampling techniques in the environmental monitoring and sustainability context. The review reiterates the conceptual soundness of point cloud upsampling in the workflow of environmental monitoring. The proposed framework reinforces the benefits of greater data richness and decision-making based on sustainability, without assuming new costs for data generation.

The paper addresses geometric and attribute distortions in point clouds, which can arise at various stages of data acquisition and processing. The authors present the main sources of distortions—such as sensor limitations, platform movement, and environmental influences—and their impact on point density, data distribution, and attribute reliability. They detail statistical methods for quantitative evaluation of distortions (e.g., Getis–Ord Gi*, Moran’s I, Gini coefficient), which allow systematic measurement and correction of errors. The study emphasizes that analyzing and mitigating distortions is essential for creating more accurate spatial models and producing reliable geoinformatics analyses.