Abstract
Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.

Abstract
Findings of Iron Age metallurgical activities related to tin metal and mining are very rare. In the present work, we present a detailed study of the Outeiro de Baltar hillfort, dated to the Late Iron Age/Early Roman period, located in a place where 20th century tin mining work took place. Elemental and microstructural analysis by portable, micro and wavelength dispersive X-ray fluorescence spectrometry (pXRF, micro-XRF and WDXRF) and scanning electron microscopy with energy dispersion spectrometer (SEM-EDS) showed that metallurgical debris found at the archaeological site is related to tin smelting and binary and ternary bronze productions. Analysis of the artefacts of diverse typologies found at the site showed that a variety of metals and alloys were in circulation and use. Samples of tin ores (cassiterite) from the region were analyzed for comparison with an archaeological tin slag from the site. The analytical results point to the production of tin metal using local cassiterite and the production of bronze by directly adding cassiterite into a smelting process. Furthermore, data of remote sensing (airborne Light Detection and Ranging (LiDAR) and historical aerial imagery) and Geographical Information System (GIS) mapping were combined with archival mining documentation and maps to retrieve a landscape context for the site. The study showed that the place of the Outeiro de Baltar hillfort (NW Iberia) was mined periodically over time.

Abstract
Northwest Iberia can be considered as one of the main areas where tin was exploited in antiquity. However, the location of ancient tin mining and metallurgy, their date and the intensity of tin production are still largely uncertain. The scale of mining activity and its socio-economical context have not been truly assessed, nor its evolution over time. With the present study, we intend to present an integrated, multiscale, multisensor and interdisciplinary methodology to tackle this problem. The integration of airborne LiDAR and historic aerial imagery has enabled us to identify and map ancient tin mining remains on the Tinto valley (Viana do Castelo, northern Portugal). The combination with historic mining documentation and literature review allowed us to confirm the impact of modern mining and define the best-preserved ancient mining areas for further archaeological research. After data processing and mapping, subsequent ground-truthing involved field survey and geological sampling that confirmed cassiterite exploitation as the key feature of the mining works. This non-invasive approach is of importance for informing future research and management of these landscapes.

Abstract
This paper presents a road surface scanning system that operates with a trichromatic line scan camera with light emitting diode (LED) lighting achieving road surface resolution under a millimeter. It was part of a project named Roadkills-Intelligent systems for surveying mortality of amphibians in Portuguese roads, sponsored by the Portuguese Science and Technology Foundation. A trailer was developed in order to accommodate the complete system with standalone power generation, computer image capture and recording, controlled lighting to operate day or night without disturbance, incremental encoder with 5000 pulses per revolution attached to one of the trailer wheels, under a meter Global Positioning System (GPS) localization, easy to utilize with any vehicle with a trailer towing system and focused on a complete low cost solution. The paper describes the system architecture of the developed prototype, its calibration procedure, the performed experimentation and some obtained results, along with a discussion and comparison with existing systems. Sustained operating trailer speeds of up to 30 km/h are achievable without loss of quality at 4096 pixels' image width (1 m width of road surface) with 250 mu m/pixel resolution. Higher scanning speeds can be achieved by lowering the image resolution (120 km/h with 1 mm/pixel). Computer vision algorithms are under development to operate on the captured images in order to automatically detect road-kills of amphibians.

Abstract
Roads have multiple effects on wildlife; amphibians are one of the groups more intensely affected by roadkills. Monitoring roadkills is expensive and time consuming. Automated mapping systems for detecting roadkills, based on robotic computer vision techniques, are largely necessary. Amphibians can be recognised by a set of features as shape, size, colouration, habitat and location. This species identification by using multiple features at the same time is known as "jizz". In a similar way to human vision, computer vision algorithms must incorporate a prioritisation process when analysing the objects in an image. Our main goal here was to give a numerical priority sequence of particular characteristics of roadkilled amphibians to improve the computing and learning process of algorithms. We asked hundred and five amateur and professional herpetologists to answer a simple test of five sets with ten images each of roadkilled amphibians, in order to determine which body parts or characteristics (body form, colour, and other patterns) are used to identify correctly the species. Anura was the group most easily identified when it was roadkilled and Caudata was the most difficult. The lower the taxonomic level of amphibian, the higher the difficulty of identifying them, both in Anura and Caudata. Roadkilled amphibians in general and Anura group were mostly identified by the Form, by the combination of Form and Colour, and finally by Colour. Caudata was identified mainly on Form and Colour and on Colour. Computer vision algorithms must incorporate these combinations of features, avoiding to work exclusively in one specific feature.

Abstract
Herdade da Contenda (HC), located in Moura municipality, Beja district (Alentejo province) in the south of Portugal (southwestern Iberia Peninsula), is a national hunting area with 5270ha. The development of an integrated system that aims to make the management of the natural and cultural heritage resources will be very useful for an effective management of this area. This integrated system should include the physical characterization of the territory, natural conservation, land use and land management themes, as well the cultural heritage resources. This paper presents a new tool for an integrated environmental management system of the HC, which aims to produce maps under a GIS open source environment (QGIS). The application is composed by a single button which opens a window. The window is composed by twelve menus (File, DRASTIC, Forest Fire Risk, Revised Universal Soil Loss Equation (RUSLE), Bioclimatic Index, Cultural Heritage, Fauna and Flora, Ortofoto, Normalizes Difference Vegetation Index (NDVI), Digital Elevation Model (DEM), Land Use Land Cover Cover (LULC) and Help. Several inputs are requires to generate these maps, e.g. DEM, geologic information, soil map, hydraulic conductivity information, LULC map, vulnerability and economic information, NDVI. Six buttons were added to the toolbar which allows to manipulate the information in the map canvas: Zoom in, Zoom out, Pan, Print/Layout and Clear. This integrated and open source GIS environment management system was developed for the HC area, but could be easily adapted to other natural or protected area. Despite the lack of data, the methodology presented fulfills the objectives.