Curriculum learning-based strategy for low-density archaeological mound detection from historical maps in India and Pakistan.

This paper presents two algorithms for the large-scale automatic detection and instance segmentation of potential archaeological mounds on historical maps. Historical maps present a unique source of information for the reconstruction of ancient landscapes. The last 100 years have seen unprecedented landscape modifications with the introduction and large-scale implementation of mechanised agriculture, channel-based irrigation schemes, and urban expansion to name but a few. Historical maps offer a window onto disappearing landscapes where many historical and archaeological elements that no longer exist today are depicted. The algorithms focus on the detection and shape extraction of mound features with high probability of being archaeological settlements, mounds being one of the most commonly documented archaeological features to be found in the Survey of India historical map series, although not necessarily recognised as such at the time of surveying. Mound features with high archaeological potential are most commonly depicted through hachures or contour-equivalent form-lines, therefore, an algorithm has been designed to detect each of those features. Our proposed approach addresses two of the most common issues in archaeological automated survey, the low-density of archaeological features to be detected, and the small amount of training data available. It has been applied to all types of maps available of the historic 1″ to 1-mile series, thus increasing the complexity of the detection. Moreover, the inclusion of synthetic data, along with a Curriculum Learning strategy, has allowed the algorithm to better understand what the mound features look like. Likewise, a series of filters based on topographic setting, form, and size have been applied to improve the accuracy of the models. The resulting algorithms have a recall value of 52.61% and a precision of 82.31% for the hachure mounds, and a recall value of 70.80% and a precision of 70.29% for the form-line mounds, which allowed the detection of nearly 6000 mound features over an area of 470,500 km2, the largest such approach to have ever been applied. If we restrict our focus to the maps most similar to those used in the algorithm training, we reach recall values greater than 60% and precision values greater than 90%. This approach has shown the potential to implement an adaptive algorithm that allows, after a small amount of retraining with data detected from a new map, a better general mound feature detection in the same map.

[Profile of maxillofacial trauma in bicycle accidents]. / Perfil del trauma maxilofacial en accidentes de bicicleta.

OBJECTIVE: To determine the maxillofacial trauma profile, concomitant results and treatment of those persons injured in bicycle accidents. Several variables were identified such as age, sex, mechanism, place, month, injury patterns and symptomatology, time for receiving medical care, and treatments. MATERIAL AND METHODS: This was an exploratory, descriptive, analytic, and retrospective study. All patients with maxillofacial trauma injury as a result of bicycle accidents were admitted to the emergency service in 2002 in the traumatology hospital Victorio de la Fuente Narváez, Mexican Institute of Social Security that covers the entire Federal District zone and some suburbs, without any distinction made as to injury specialty. RESULTS: Patients (127) with maxillofacial trauma from bicycle accidents were identified. Children aged 6 to 10 years were the most affected. There was more injury in soft tissue in the midfacial area, such as contusions, injuries dermabrasion and some fractures resulting in nose trauma. Most of the accidents happened in the streets by falling from the bicycles. Forty six persons had more than two maxillofacial injuries. Head trauma with cervically projected sprain was demonstrated as associated trauma. More than 50% of the patients received immediate treatment under local anesthesia by maxillofacial surgery or orthopedics and traumatology during the afternoon and night. CONCLUSIONS: Prevention programs for parents and children in homes, schools and medical care facilities should be established to encourage traffic education and the use of protective headgear.