Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future.

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute).

Sediment transport is a key element in intertidal beach morphodynamics, but measurements of sediment transport are often unreliable. The aim of this study is to quantify and investigate cross-shore sediment transport and the resulting topographic changes for a tide-dominated, sandy beach. Two fortnight-long field experiments were carried out during which hydrodynamics and sediment dynamics were measured with optical and acoustic sensors, while the beach topography was surveyed with a permanent terrestrial laser scanner. Suspended sediment was generally well-mixed and currents were largest at approximately 1.5 m above the bed, which resulted in a peak in sediment transport at 1/3 of the high tide level. The mean transport direction was onshore during calm conditions (wave height <0.6 m) thanks to tidal currents and offshore during energetic conditions due to undertow. Oscillatory transport was always onshore because of wave asymmetry but it was subordinate to mean transport. The intertidal zone showed an alternation of erosion and accretion with formation of morphological features during energetic (no storm) conditionsand smoothening of the morphology during calm conditions. A good qualitative and quantitative agreement was found between the daily cross-shore suspended load and beach volume changes, especially during calm conditions.

The integration of large language models (LLMs) with geographic information science (GIScience) represents a new frontier in interdisciplinary research that combines advanced natural language processing with sophisticated spatial data analysis. This paper explores the synergistic potential of combining the natural language understanding and generation capabilities of LLMs with the expertise of GIScience in handling complex geospatial data. By exploring the specific contributions that LLMs can offer to GIScience, such as improving data processing, analysis, and visualization, and the mutual benefits that GIScience can offer to LLMs in terms of spatial reasoning and conceptual frameworks, we outline a comprehensive framework and a research agenda for this integration. Furthermore, we address the societal and ethical implications of this convergence, highlighting the challenges of bias, misinformation, and environmental impact. Through this exploration, we aim to set the stage for innovative applications in urban planning, environmental analysis, and beyond, while emphasizing the need for responsible use of AI.

The digital age has brought about an explosion in the growth of data, of which data with a geographical component stands out. Proper use of geographical data comes with the need for correct coordinate reference systems (CRSs). They are considered the ultimate binder for interoperability between geospatial data actors and stakeholders. Moreover, CRSs are crucial for the visual and analytical integration of geospatial data from disparate data sources. However, CRSs might be—for numerous reasons—incorrectly assigned or even missing. The result is a time-consuming study of the map, literature, and available resources to ultimately find the alleged right CRS. This study provides a summary of prevailing resources from national mapping agencies of some European countries to address the above problem. Secondly, and most importantly, is the development of an open-source Python-based software package. This software package aims to accurately estimate the best candidate CRS, given a tuple of coordinates at a priori an approximately known location. It is controlled by geocoding the known location and intersecting the resulting coordinate with the bounding box of all CRSs in the EPSG-database. An in-depth review of CRS tools by mapping authorities reveals, in particular, limitations concerning the countries’ spatial areas, in combination with often required know-how of local CRSs. To address these shortcomings, our tool is developed to enable a more generic extraction of CRSs for any given location worldwide. Testing proved successful for 30 different maps, with a grid present on the map and the CRS of the map being included in the EPSG-database.

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial.