Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

The topic of underwater (UW) image colour correction and restoration has gained significant scientific interest in the last couple of decades. There are a vast number of disciplines, from marine biology to archaeology, that can and need to utilise the true information of the UW environment. Based on that, a significant number of scientists have contributed to the topic of UW image colour correction and restoration. In this paper, we try to make an unbiased and extensive review of some of the most significant contributions from the last 15 years. After considering the optical properties of water, as well as light propagation and haze that is caused by it, the focus is on the different methods that exist in the literature. The criteria for which most of them were designed, as well as the quality evaluation used to measure their effectiveness, are underlined.

The task of colour restoration on datasets acquired in deep waters with simple equipment such as a camera with strobes is not an easy task. This is due to the lack of a lot of information, such as the water environmental conditions, the geometric setup of the strobes and the camera, and in general, the lack of precisely calibrated setups. It is for these reasons that this study proposes a self-adaptive colour calibration method for underwater (UW) images captured in deep waters with a simple camera and strobe setup. The proposed methodology utilises the scene’s 3D geometry in the form of Structure from Motion and MultiView Stereo (SfM-MVS)-generated depth maps, the well-lit areas of certain images, and a Feedforward Neural Network (FNN) to predict and restore the actual colours of the scene in a UW image dataset.

Underwater photogrammetry is challenged by image degradation caused by water absorption and scattering, which negatively impacts feature detection and 3D reconstruction quality. This study aims to evaluate the effectiveness of a Feedforward Neural Network-based colour correction method designed to enhance underwater image quality, thereby improving feature matching and subsequent 3D reconstruction processes. The proposed approach leverages deep learning to correct colour distortions without relying on physical models of underwater light propagation. Evaluation was performed using established feature detection algorithms, such as SIFT and SURF, applied to multiple underwater datasets capturing diverse imaging conditions. The goal is to determine whether neural network-based colour correction can increase the number of valid feature correspondences, improve sparse and dense point cloud generation, and ultimately support more accurate and robust 3D reconstructions. By integrating this correction method within a photogrammetric workflow, the study investigates the potential benefits and limitations of data-driven colour enhancement in underwater environments. The findings are intended to inform future development of hybrid approaches that combine physical modelling with deep learning, aiming to optimize both visual clarity and geometric fidelity in underwater mapping and documentation. This work contributes to advancing underwater photogrammetry by addressing critical challenges related to image quality and reconstruction accuracy, with implications for archaeological surveys, marine research, and underwater infrastructure inspection.

The fusion of geomatic techniques with different accuracy and resolution has been used in recent years to applications of geometrical documentation for several archaeological sites. In this research article, the case of the Roman Baths of Amathus in Limassol, Cyprus was investigated. Aerial photogrammetry and Terrestrial Laser Scanning (TLS) techniques were used to produce the final product. The photogrammetric data were based on Post-Processed Kinematic (PPK) imagery while the TLS georeference was based on targets. The main research objective of this paper is to examine the reliability of PPK photogrammetric data without the use of Ground Control Points (GCPs) and how well they can be integrated with TLS data. Also, the two acquisition techniques were compared and indicated that 0.005 m Ground Sampling Distance (GSD) resolution aerial images and 0.0061/10 m resolution scans can be qualitatively fused. The research paper will present the entire methodology up to the generation of the final 3D photorealistic product.

This study investigates photogrammetric solutions for surveying confined underground environments using a small and affordable 360° camera, applied in Tomb 4 at the UNESCO World Heritage Site, Tombs of the Kings in Paphos, Cyprus. Conducted within the framework of the ENGINEER project, this study evaluates the performance of a low-cost 360° 25-lens camera by comparing it to a terrestrial laser scanner (TLS) system used as a reference. The primary objective is to assess the suitability of the 360° camera for 3D documentation in restricted spaces, and to explore its potential application in conservation and restoration efforts at archaeological sites. Two different approaches were tested: using the camera images as 360° panoramic photos or as 25-photos rigid rig setup. The study analyses the accuracy of these methods by generating Point Clouds (PCs) and comparing them to the TLS reference dataset using metrics such as cloud-to-cloud distance, surface roughness, and PC density. In addition, a visual assessment was conducted.