Conference Agenda

Existing nationwide spatial datasets do not adequately represent Germany’s traffic infrastructure. They are often fragmented, vary in quality, and lack documentation on acquisition methods and spatial coverage. This is especially true for spatial data on parking areas, despite the growing demand for such information in traffic management and urban planning. The topic is gaining importance in the context of repurposing on- and off-street parking in cities (Thigpen and Volker, 2017). While some large cities like Berlin maintain spatial parking inventories, most smaller cities do not. In Berlin, data is collected via vehicle-mounted camera systems (Senatsverwaltung für Mobilität, Verkehr, Klimaschutz und Umwelt, 2023). However, this approach only maps public parking areas, is costly in time and resources, and excludes private and semi-private spaces. Large-scale mapping using aerial imagery can help to bridge this gap, enabling efficient coverage from city to national scale. This study presents a novel parking area inventory for Berlin derived from aerial imagery by AI-based image analysis methods. Results include public, semi-private, and private parking areas, enriched with information on accessibility and capacity.

The methodology is based on aerial imagery with a ground sampling distance of 10 cm, such as those provided for example by the German state surveying offices (DOP10). Traffic areas are segmented using a widely-successful and robust segmentation model based on the U-Net and DenseNet architectures (Henry et al., 2021). It leverages the ability from the former to extract fine-grained information from every feature level of input images, while benefitting from the latter’s efficient information flow and optimization in both the encoder and decoder. It was trained and validated on the novel TIAS (Traffic Infrastructure and Surroundings) dataset. TIAS consists of 51 manually annotated images from diverse traffic environments across German cities – 45 acquired using DLR’s 3K and 4K camera systems, and six DOP10 images from public authorities. It includes nine classes: road, access way, bikeway, footway, keep-out area, parking area, railroad bed, road shoulder, and water (Merkle et al., 2024). For this study, only the classes parking area, road, and access way were segmented.

The resulting segmentation maps are vectorized, smoothed, and imported into a PostGIS database. Each parking polygon is assigned an access category by intersecting with cadastral data on land parcel usage (Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen Berlin, 2025). These usage types are grouped into public, semi-private, and private access levels and applied to the respective polygons.

To estimate the number and positions of parking spots from each vectorized parking polygon, the type of parking – parallel, diagonal, or vertical – must be determined, as each type requires different dimensions according to German parking layout standards (FGSV, 2023). The estimation involves three steps: (1) extracting centerlines of roads and accessways, (2) identifying vehicles and their orientation relative to the road, and (3) assigning a parking type based on average orientation of vehicles on a parking polygon.

Centerlines of each road and accessway polygon are extracted using PostGIS’s ST_VoronoiLines function, followed by a three-stage filtering process to remove support lines towards the polygon edges. This yields one centerline per polygon. Light- and heavy-duty vehicles are detected using a transformer-based object detection framework – DINO, as adapted by Mühlhaus et al. (2023) for oriented bounding box detection in the aerial imagery domain, replacing the standard horizontal bounding box representation. The model was trained on the Eagle dataset – an aerial dataset for vehicle detection (Azimi et al., 2020). The detected objects are subsequently stored in a PostGIS database for further analysis. The longitudinal axis of each vehicle is extended five meters front and back, then intersected with centerlines of roads and accessways to obtain the intersection angle. For vehicles without direct intersections, the mean deviation between their orientation and nearby centerline segments within a buffer is used. This is especially relevant for parallel parking.

The mean orientation of all vehicles on a parking polygon defines its dominant orientation. Based on this angle, parking types are classified as: parallel (0°–30°), diagonal (31°–75°), and vertical (76°–90°). Parking capacity is calculated by dividing the area of the polygon by standard slot dimensions from FGSV (2023): parallel (5.5 m × 2.15 m), diagonal (5.42 m × 2.7 m), and vertical (5.14 m × 2.7 m). For polygons without identified parking type, a default slot size of 5.2 m × 2.4 m is used.

The resulting Berlin-wide inventory comprises 1,333,953 parking spots. Of these, 60% are classified as publicly accessible, 21% semi-private, and 19% private. This reveals that inventories limited to public parking underestimate total availability by around 40%. Regarding layout, 36% of the spots are parallel, 27% diagonal, and 20% vertical; 17% could not be classified due to lack of vehicle data.

This study demonstrates the potential of aerial imagery for generating comprehensive parking inventories. The method is efficient, highly automated, and scalable, making it applicable to larger areas. Moreover, the results highlight the limitations of current approaches that only capture public parking. Future work will address larger-scale processing, integration of underground parking data, and development of correction factors for partially occluded areas.

Thailand is a significant emitter of greenhouse gases (GHGs), with total emissions reaching approximately 385,941.14 ktCO₂eq in 2022. Of this, 77,021.31 ktCO₂eq, or 30.29% of emissions from the energy sector, originated from the transportation sector. The Thai government has prioritized mitigation efforts by promoting Battery Electric Vehicles (BEVs), particularly the transition to Battery Electric Buses (BEBs) in public transportation. While electrification initiatives are underway, the lack of a spatially guided approach to infrastructure placement along intercity bus corridors remains a critical gap. This study aims to estimate the spatial charging demand of Thailand's intercity bus network and identify potential infrastructure locations to support the adoption of Battery Electric Buses (BEBs). Potential charging points were determined based on the typical operational range of BEBs and interpolated along intercity bus routes. These points were then used to evaluate candidate infrastructure locations through a Charging Demand Score (CDS), an indicator that quantifies the relative demand for charging infrastructure at the grid-cell level across the study area. The results highlight several provinces with notably high charging demand, particularly along major intercity corridors in the North, Northeast, and South. These findings provide a valuable foundation for designing data-driven policies to support the electrification of Thailand's public transportation system.

The extraction of urban functional areas plays a critical role in data-driven policymaking. While previous studies have primarily focused on general-purpose functional area extraction, this study proposes a novel methodology for identifying bicycle-specialized functional areas by integrating spatial network analysis with semantic POI embedding. Using Seoul, South Korea, as a case study, we first constructed linear spatial units by applying a network Voronoi algorithm to the city’s bicycle road network and public bicycle station data. Next, POI data were classified according to bicycle trip purposes and embedded using a Word2Vec-based approach to capture high-dimensional semantic features. These features were then aggregated for each spatial unit, with weights assigned based on POI type and proximity to bicycle roads. Finally, K-means clustering was conducted to extract distinct functional areas optimized for bicycle usage. The experimental results identified four unique cluster types, including residential-centered and park-oriented zones, demonstrating the effectiveness of the proposed methodology in supporting bicycle-friendly urban planning. This approach offers valuable insights for public bicycle redistribution, infrastructure deployment, and sustainable mobility policy.

Rapid urbanization and environmental changes necessitate precise and scalable methods for detecting building changes in satellite imagery, critical for urban planning, disaster response, and smart city analytics. Traditional approaches, relying on handcrafted features or shallow learning algorithms, often fail to capture the complex spatio-temporal patterns inherent in high-resolution remote sensing data. Recent advancements in deep learning, particularly convolutional neural networks (CNNs) and Transformer architectures, have significantly improved the ability to model these intricate relationships, enabling more accurate detection of structural changes (Chen et al., 2022). However, these methods typically require extensive labeled datasets and domain-specific fine-tuning, limiting their scalability and adaptability across diverse scenarios. The emergence of vision-language models (VLMs), which integrate textual and visual information, offers a promising avenue to enhance contextual understanding and focus on task-relevant features (Tao et al., 2025). Despite these advances, adapting pretrained VLMs to remote sensing tasks, such as building change detection, remains a significant challenge due to differences between natural imagery and satellite data.

In this study we propose a novel framework that combines Grounding DINO (Liu et al., 2023), a pretrained VLM, with a hybrid CNN-Transformer architecture. Our approach leverages text-informed preprocessing to generate building masks, which guide a lightweight ResNet18 (He et al., 2016) backbone and a custom Transformer encoder to focus on structural and spectral changes. By amplifying building-related features and employing a combined loss function, our framework achieves promising performance on a benchmark change detection dataset. The proposed method minimizes reliance on extensive finetuning, offering a scalable solution for urban monitoring and Earth observation applications.

Floods and drought are one of the most recurring and devastating natural hazards threatening life and economy. Early warning of the likely occurrence of flood and drought disaster risks and impacts could assist in decision making to support proper disaster risk responses, management and formulation of informed Climate Change adaptation and mitigation strategies for enhanced resilience and disaster risk reductions. Globally and across Africa various initiatives and innovations of multi-hazard early warning systems based on web and mobile information platforms have been developed, however, none fit in the Tanzanian and East African environment. Developing a localized multi-hazard early warning for climate related hazards is essential in reducing loss of life and damage to property. This study aimed at developing an innovative multi-hazard early warning Web based Geographical Information System (GIS) App for flood and drought disaster risks in East Africa, Case of Tanzania.

To develop the Web -Based GIS App, the guiding research questions are centred on user requirement elicitation, identification of vulnerable communities, development of the Web-based GIS App conceptual model and the Web-Based GIS App. For the Web-based GIS App requirement elicitation a thorough document review and stakeholders’ workshop was used. To identify and map the floods and drought vulnerable areas we used a Height Above Nearest Drainage (HAND) model and Vegetational condition index (VCI) were utilized. Thereafter, we used the Palmer Drought Severity Index (PDSI) and deep learning neural networks based on geospatial weather data using convolution long-short-term memory (ConvLSTM) model to predict drought in Dodoma. The Web based GIS App was designed and developed using the RAD methodology in Microsoft .NET framework.

From analysis of flood prone areas indicated Magomeni, Kigogo, Mchikichini, Mburahati, Mabibo, Ndugumbi, Kijitonyama Makumbusho, Mikocheni, Kipawa, kiwalani, Kawe, Kunduchi, Mbweni, Chanika, Pembamnazi, Ksarawe II and Mjimwema, Somangila, Hananasifu, Tandale and Msasani wards as areas which are highly prone to flooding. The results were validated using observed flood points in Tandale wards and flood zones which were identified and mapped using the participatory approach by vulnerable community members in Hanansifu and Msasani Wards. On the other hand, the analysis of drought conditions in Dodoma region depicts Chamwino, Bahi and Central Dodoma to be highly prone to drought risks. Thereafter, the Web-based GIS conceptual model was developed followed by development of the Web-based GIS for dissemination of climate related early warning information for flood and drought disaster risks. We recommend integration of GIS and Early Warning Tools into Existing Policies, Establish Monitoring and Evaluation Frameworks and further Improvement of the Developed App for enhanced Disaster Risk reductions.