Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

The challenges associated with climate change are increasing, so there is an urgent need for modern tools to effectively assess, predict and minimise climate risks. This research paper presents the results of the development of the innovative Community Climate Change Impacts Service (C3IS) system, which represents a paradigm shift in climate risk analysis. C3IS is a module that includes a pioneering set of tools with an interactive application programming interface (API) fully integrated with the Google Earth Engine (GEE) platform. The C3IS module enables lightning-fast collection and visualisation (in real time) of critical climate risk data through flexible integration with GEE. The advantages of this integration are the ability to use the GEE platform to access an extensive petabyte-scale catalogue of geospatial data and an ever-expanding database of satellite imagery. The defining feature of the developed module is accessibility and usability due to the exclusion of operations such as the time-consuming preliminary processing of “big data”; complex modelling; and large-scale data storage. The study shows the promising application of the C3IS module for the operational decision making and development of sound strategies for effective climate change mitigation.

In spite of the tremendous success in artificial intelligence technology and a high level of automation in geospatial data obtaining processes, there is still a need for topographical field data collection by professional surveyors. Understanding terrain topology and topography is a cognitive skill set that has to be demonstrated by geospatial Subject Matter Experts (SME) for the productive work in the topographic surveying field. For training of the mentioned above skillset, one has to be exposed to the theory and must also practice with surveying instruments in field conditions. The challenge of any surveying/geospatial engineering workforce training is to expose students to field conditions which might be limited due to equipment expenses and meteorological conditions that prevent good data collection. To meet this challenge, the Integrated Geospatial Technology research group is working on a geospatial virtual reality (VR) project which encompasses the following components: (a) immersive visualization of terrain; (b) virtual total station instrument; (c) virtual surveyor with reflector installed on the virtual rod. The application scenario of the technology we are working with has the following stages: (1) student is installing total station on the optimal location; (2) students move virtual surveyor on the sampling points they consider to be important (3) contours are generated and displayed in 3D being superimposed on 3D terrain; (4) accuracy of terrain modeling is observable and measurable by comparing the sampling model with initial one.

The intensifying effects of climate change have led to increased flooding, even in desert regions, resulting in significant socio-economic and ecological impacts. This study analyzes the causes and consequences of flooding in the Zhem River basin using data from ground stations, including Kazhydromet, and satellite platforms such as USGS FEWS NET and Copernicus. Spatial analyses conducted in ArcGIS utilized classified raster data to map the dynamics of flooding, snow cover, vegetation, and soil conditions. This enabled a geoecological analysis of flood damage on the vital components of the local landscape. Results show that flooding in the Zhem River basin was driven by heavy winter precipitation, rapid snowmelt, and a sharp rise in spring temperatures. The flood damaged Kulsary city and also harmed the region’s soil, vegetation, and wildlife. In July 2024, the flooded sail area tripled compared to the same period in 2023. Additionally, the area of barren land or temporary water bodies (pools) formed three months after the water receded also tripled, increasing from 84.9 km² to 275.7 km². This study highlights the critical need for continued research on the long-term environmental effects of flooding and the development of adaptive management strategies for sustainable regional development.

With the advent of GPS/GNSS satellite navigation systems and Unmanned Aerial Systems (UAS) surveying profession is nowadays facing its transformative stage. Written by a team of surveying experts, Surveyor’s Instruments and Technology gives surveying students and practitioners profound understanding of how surveying instruments are designed and operating based on surveying instrument functionality. The book includes the required basic knowledge of accurate measurements of distances and angles from theoretical principles to advanced optical, mechanical, electronic and software components for comparative analysis. Readers are presented with basic elements of UAS systems, practical interpretation techniques, sensor components, and operating platforms. Appropriate for surveying courses at all levels, this guide helps students and practitioners alike to understand what is behind the buttons of surveying instruments of all kinds when considering practical project implementations.

With the rapid shifts in environmental conditions, accurately predicting soil erosion has become crucial for the sustainable management of land resources. This study introduces a deep learning-based approach to forecast soil erosion risks in Western Kazakhstan up to 2030, focusing on the LS factor defined by the Universal Soil Loss Equation (USLE). High-resolution digital elevation models (DEMs) from ASTER GDEM and historical data on climate and land use were utilized to train a convolutional neural network (CNN), enabling projections of future LS-factor changes and the corresponding erosion risks. To further improve the accuracy of LS-factor calculations, the System for Automated Geoscientific Analyses (SAGA) was applied using a multiple-flow algorithm. The results forecast a significant rise in erosion risk by 2030, with areas having LS values between 8 and 24 expected to increase by 10%, and those with LS values above 24 by 0.05%, potentially affecting an additional 24,000 km². The model achieved a 92% accuracy rate, underscoring the effectiveness of deep learning in environmental risk analysis. The integration of SAGA results provides a more detailed understanding of the erosion processes, enhancing the precision of the predictions.