In true feline style, cats took their time in deciding when and where to forge bonds with humans. According to new scientific evidence, the shift from wild hunter to pampered pet happened much more recently than previously thought - and in a different place. A study of bones found at archaeological sites suggests cats began their close relationship with humans only a few thousand years ago, and in northern Africa not the Levant. They are ubiquitous, we make TV programmes about them, and they dominate the internet, said Prof Greger Larson of the University of Oxford. That relationship we have with cats now only gets started about 3.5 or 4,000 years ago, rather than 10,000 years ago.

By upgrading an existing deep learning model with the knowledge provided by one of the oldest sets of grayscale satellite imagery, known as CORONA, we improved the AI model's attitude towards the automatic identification of archaeological sites in an envir onment which has been completely transformed in the last five decades, including the complete destruction of many of those same sites. The initial Bing - based convolutional network model was re - trained using CORONA satellite imagery for the district of Abu Ghraib, west of Baghdad, central Mesopotamian floodplain. The results were twofold and surprising. First, the detection precision obtained on the area of interest increased sensibly: in particular, the Intersection - over - Union (IoU) values, at the image segmentation level, surpassed 85%, while the general accuracy in detecting archeological sites reached 90%. Second, our re - trained model allowed the identification of four new sites of archaeological interest (confirmed through field verification), previously not identified by archaeologists with traditional techniques. This has confirmed the efficacy of using AI techniques and the CORONA imagery from the 1960s to discover archaeological sites currently no longer visible, a concrete breakthrough with significant consequences for the study of landscapes with vanishing archaeological evidence induced by anthropization.

This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human-AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotation

We are looking for a highly motivated candidate who holds a Master of Science in Geography, Geology, Archaeology or Natural Sciences. You will be required to carry out fieldwork in the Bolivian Amazon and travel there for a period of 2 to 3 months during the dry seasons (July - October) of 2023 and 2024. The field work will involve flying a drone with a LIDAR over approx. Back in Barcelona your work will focus on building a database of the archaeological sites, calculating the volume of each site and analysing their patterns and properties. You are expected to publish at least 3 papers in well-known scientific journals by the end of the 4 yrs position.

In recent months there has been talk about how artificial intelligence can create images from textual prompts. Therefore, when one associates the words artificial intelligence and art, one immediately thinks of DALL-E, Stable Diffusion, and other algorithms. In this article, instead, I want to discuss why artworks are often less safe than we think, and how artificial intelligence can help preserve them. "Every act of creation is first of all an act of destruction." It is a mistake to think that cultural heritage is safe. Many of humanity's most valuable works are also among the most fragile. Throughout history, only a fraction of works of art has managed to survive over time. For example, during wars, cultural heritage is often damaged.

KAUST scientists have used deep learning algorithms to accelerate the examination of thousands of years old, giant, stone rectangles in the Saudi desert. "An international study showed that the huge, mysterious stone structures known as'Mustatil' (Arab word for'Rectangle') in northwestern Saudi Arabia, are among the oldest archeological ruins in the world," Saudi Minister of Culture, Prince Badr bin Abdullah bin Farhan, said in a tweet in 2021. These historic sites, which are around 7,000 years old, bewildered researchers and scientists who have long sought to determine their nature and the reasons behind their construction. A recent study by the University of Cambridge suggested that these huge structures, comprising chambers, entrances, and seats, are more complicated than expected. For quicker results, researchers at the King Abdullah University of Science and Technology (KAUST) have used an artificial intelligence network to carry out a detailed geological survey in the region, which hasn't been sufficiently studied so far.

The nearby volcano blackened the sky and swallowed the city in clouds of ash; centuries later, robot dogs now prowl the ruins, guarding the city's dead against the ravages of time. Boston Dynamics' robot dog, Spot, will help archaeologists and preservation crews by patrolling the 66-hectare site for signs of erosion, damage, and looting. The volcanic ash that buried Pompeii in 79 CE turned a thriving Roman coastal city into a well-preserved tomb--and a time capsule. Today, the archaeological site is one of the most famous in the world, and it continues to reveal new glimpses of life in a cosmopolitan Roman city during the empire's heyday, like an ancient fast-food counter excavated in 2020. But in 2013, UNESCO (the United Nations Educational, Scientific and Cultural Organization) found that erosion and weathering were taking a toll on the parts of the site archaeologists had excavated so far. To protect the ruined city and keep restoration workers safe, park authorities needed to find new ways to monitor for damage, restore ancient structures, and preserve them for the future.

UAVs have found an important application in archaeological mapping. Majority of the existing methods employ an offline method to process the data collected from an archaeological site. They are time-consuming and computationally expensive. In this paper, we present a multi-UAV approach for faster mapping of archaeological sites. Employing a team of UAVs not only reduces the mapping time by distribution of coverage area, but also improves the map accuracy by exchange of information. Through extensive experiments in a realistic simulation (AirSim), we demonstrate the advantages of using a collaborative mapping approach. We then create the first 3D map of the Sadra Fort, a 15th Century Fort located in Gujarat, India using our proposed method. Additionally, we present two novel archaeological datasets recorded in both simulation and real-world to facilitate research on collaborative archaeological mapping. For the benefit of the community, we make the AirSim simulation environment, as well as the datasets publicly available.

Citizen scientists searching aerial images while on coronavirus lockdown have uncovered dozens of previously-hidden Roman, prehistoric and medieval sites. Archaeological digs are currently on hold due to the pandemic but researchers have found roads, burial mounds and settlements - all while working from home. Researchers from the University of Exeter asked teams of volunteers to search through LiDAR images and aerial surveys to hunt for signs of ancient sites. Volunteer amateur archaeologists cross-referenced these topographical images of the Tamar Valley that highlight hidden features with historic maps of the area. Lead researchers Dr Chris Smart said they were'redrawing the archeological map of the South West' and getting a better idea of how areas developed over millennia.

Archaeologists have uncovered scores of long-abandoned settlements along coastal Madagascar that reveal environmental connections to modern-day communities. They have detected the nearly indiscernible bumps of earthen mounds left behind by prehistoric North American cultures. Still other researchers have mapped Bronze Age river systems in the Indus Valley, one of the cradles of civilization. All of these recent discoveries are examples of landscape archaeology. They're also examples of how artificial intelligence is helping scientists hunt for new archaeological digs on a scale and at a pace unimaginable even a decade ago.