Historical topographic maps are an important source of a visual record of the landscape, showing geographical elements such as terrain, elevation, rivers and water bodies, roads, buildings, and land use and land cover (LULC). Historical maps are scanned to their digital representation, a raster image. To quantify different classes of LULC, it is necessary to transform scanned maps to their vector equivalent. Traditionally, this has been done either manually, or by using (semi)automatic methods of clustering/segmentation. With the advent of deep neural networks, new horizons opened for more effective and accurate processing. This article attempts to use different deep‐learning approaches to detect and segment wetlands on historical Topographic Maps 1: 10000 (TM10), created during the 50s and 60s. Due to the specific symbology of wetlands, their processing can be challenging. It deals with two distinct approaches in the deep learning world, semantic segmentation and object detection, represented by the U‐Net and Single‐Shot Detector (SSD) neural networks, respectively. The suitability, speed, and accuracy of the two approaches in neural networks are analyzed. The results are satisfactory, with the U‐Net F1 score reaching 75.7% and the SSD object detection approach presenting an unconventional alternative.

Spherical cameras are being used more frequently in surveying because of their low cost and possibility to process spherical images with conventional SfM methods. Fish-eye lenses are now frequently found on modern 360° cameras, allowing for the capture of the entire scene and subsequent model reconstitution by a non-photogrammetrist specialist. Gyrospherics are a feature of cameras that ensure image stabilization to reduce blur when the camera is moving. This feature allows it to capture moving scenes in time-lapse mode. However, two main factors - hardware parameters and software algorithms - affect the quality of the images that are captured. The 360° camera design allows for a variety of data processing methods by SfM. These images were created using multiple image sensors and lenses from each 360° camera. These images can be processed individually or by applying rules to define relationships between images. Also, images from cameras can be stitched and processed with a spherical camera model. In this paper is proposed processing methods of data from 360° cameras and estimated accuracy of each method.

The article delves into the application of laser scanning within the realm of engineering, concentrating its examination on rotary kilns. Through an in-depth case study, this article meticulously assesses the advantages inherent in utilising laser scanning technology and draws comparisons with conventional measurement methods. The overarching objective of the investigation is to unravel the intricate relationship between the deterioration of the rotary kiln liners and the ovality of the underlying steel structure. By meticulously analysing these aspects, this article seeks to contribute valuable insights into the understanding of this complex interplay in the context of engineering practices. As a measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of the cylindrical shells. The research uncovered significant disparities in inner lining loss between the sections. A correlation was found between the ovality and elimination of the inner lining. Due to the hypothesis of constant inner lining loss in the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the wells. This study offers significant information on the maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.

Historical maps contain valuable topographic information, including altimetry in the form of annotated elevation contours. These contours are essential for understanding past terrain configurations, particularly in areas affected by human activities such as mining or dam construction. To make this data usable in modern GIS applications, the contours must be vectorized—a process that often requires extensive manual work due to noise, inconsistent symbology, and topological disruptions like annotations or sheet boundaries. In this study, we apply a convolutional neural network (U-Net) to improve the automation of this vectorization process. Leveraging a recent benchmark for historical map vectorization, our method demonstrates increased robustness to disruptive factors and reduces the need for manual corrections.

Accurate digital documentation of heritage structures is vital for conservation, restoration, and structural analysis. Traditional methods for analyzing masonry are time-consuming and subjective. This study proposes a deep learning-based approach using a U-Net convolutional neural network to automatically segment stone and mortar in heritage masonry, trained on high-resolution imagery of Prague’s Old Town Bridge Tower. Unlike prior studies focused on distinct brick structures, our dataset presents a greater challenge due to the similar textures of stone and mortar. Data was collected using a DJI M300 drone with a P1 camera and an RTC360 laser scanner, capturing the entire tower and its interior. The resulting 3D reconstructions and orthophotos, with a 1 mm ground sampling distance, enabled precise manual segmentation of all stones, excluding non-masonry features. After splitting the available manually annotated data, U-Net models with differing parameters were trained on the train set and evaluated on a test set, achieving a class-averaged F1 score of up to 85.58%. The created segmentation maps can be easily converted to finished vector drawings. Results show that deep learning significantly improves segmentation speed and consistency over manual methods. These maps support conservation tasks such as structural monitoring and damage detection. The trained model will aid future documentation of the Charles Bridge, illustrating the potential of AI in advancing scalable, objective digital heritage conservation.