## 1 Introduction
In recent decades, there has been an increase in studies addressing the impact of climate change on cultural heritage, particularly in Europe, but also in other countries like China [7]. Research constantly shows that floods, heavy rains, and periods of drought are becoming more frequent. For example, [PERSON] et al. [8] emphasize the need for optimized management strategies to mitigate the effects of climate-induced disasters. Similarly, [PERSON] et al. [9] focus on safeguarding cultural heritage from culture-related hazards by adopting integrated risk assessment frameworks and co-operation across regions. [PERSON] and [PERSON] [10] provide a broader review of how climate change is affecting open-air heritage, where rising global temperatures and sea-level changes threaten archeological sites, historic landscapes, and vermacular architecture. The idea of the ChemiNova project is to use existing technologies and create tools that have different uses within conservation and cultural heritage. The project is primarily people-centered, so any system will be intended for use by both heritage professionals and non-expert citizens. This system will combine 3D modeling, artificial intelligence, augmented reality, mixed reality, photogrammetry, and spectral data, among other elements. Currently, building damage maps are widely used after catastrophes such as earthquakes, and automated methodologies [11] and photogrammetry are used for the production of damage maps or the detection of damage, based on 3D point clouds [12]. In recent years, artificial intelligence has become a common tool in our lives. It is also finding its way into documentation processes in conservation and restoration. Recent research shows that thanks to machine learning, artificial intelligence is able to detect certain forms of damage on construction sites with high accuracy rates [13; 14; 15]. Unlike other studies involving ARTDET, which uses high-resolution images annotated by experts [13], in ChemiNova, we are not only using high-resolution RGB images for machine learning but also introducing spectral data from a hyperspectral camera, which adds information for damage characterization. This will mean that the data and characterization obtained with AI will be much more accurate, as it will have information on damage recorded in both dimensions by various sensors, along with non-invasive image analysis such as multiband imaging and hyperspectral imaging. Going a step further, this research focuses on the work prior to automatic damage detection or AI training for assisting visual inspections of cultural heritage [14; 16], such as the extraction of contours to provide a basis for damage mapping. As yet, the first tests have been carried out with easel-mounted works belonging to the [PERSON] contemporary art collection of the Universitat de Valencia. These artworks were affected by the floods in the province of Valencia at the end of October 2024. Climate change not only causes slow cumulative damage but also results in extreme phenomena, as can be seen in the study by [PERSON] and [PERSON] [17]. These events show how climate change impacts directly and most clearly on the materials of which the work is made, i.e., on the tangible part [18]. Edge detection is a fundamental tool in the fields of image processing and computer vision. This tool is primarily used to identify points in a digital image where the brightness changes sharply. It is often the case that these points correspond to object boundaries. Thus, it is a crucial step in a variety of tasks, including object recognition, image segmentation, and scene analysis [19]. Recent efforts have also explored the optimization of classical edge detection techniques. As an example, [PERSON] and [PERSON] [20] present an optimized edge detection method that enhances the classical Canny algorithm by integrating integral image detection to accelerate pyramid construction and uses Gaussian separation to simplify the filtering process.