Geographic features recognition for heritage landscape mapping – Case study: The Banda Islands, Maluku, Indonesia

This study examines methods of geographic features recognition from historic maps using CNN and OBIA. These two methods are compared to reveal which one is most suitable to be applied to the historic maps dataset of the Banda Islands, Indonesia. The characteristics of cartographic images become the main challenge in this study. The geographic features are divided into buildings, coastline, and fortress. The results show that CNN is superior to OBIA in terms of statistical performance. Buildings and coastline give excellent results for CNN analysis, while fortress is harder to be interpreted by the model. On the other hand, OBIA reveals a very satisfying result is very depending on the maps’ scales. In the aspect of technical procedure, OBIA offers easier steps in pre-processing, in-process and post-processing/finalisation which can be an advantage for a wide range of users over CNN

Potential of deep learning segmentation for the extraction of archaeological features from historical map series

Historical maps present a unique depiction of past landscapes, providing evidence for a wide range of information such as settlement distribution, past land use, natural resources, transport networks, toponymy and other natural and cultural data within an explicitly spatial context. Maps produced before the expansion of large‐scale mechanized agriculture reflect a landscape that is lost today. Of particular interest to us is the great quantity of archaeologically relevant information that these maps recorded, both deliberately and incidentally. Despite the importance of the information they contain, researchers have only recently begun to automatically digitize and extract data from such maps as coherent information, rather than manually examine a raster image. However, these new approaches have focused on specific types of information that cannot be used directly for archaeological or heritage purposes. This paper provides a proof of concept of the application of deep learning techniques to extract archaeological information from historical maps in an automated manner. Early twentieth century colonial map series have been chosen, as they provide enough time depth to avoid many recent large‐scale landscape modifications and cover very large areas (comprising several countries). The use of common symbology and conventions enhance the applicability of the method. The results show deep learning to be an efficient tool for the recovery of georeferenced, archaeologically relevant information that is represented as conventional signs, line‐drawings and text in historical maps. The method can provide excellent results when an adequate training dataset has been gathered and is therefore at its best when applied to the large map series that can supply such information. The deep learning approaches described here open up the possibility to map sites and features across entire map series much more quickly and coherently than other available methods, opening up the potential to reconstruct archaeological landscapes at continental scales

Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps

Historical maps provide useful spatio-temporal information on the Earth's surface before modern earth observation techniques came into being. To extract information from maps, neural networks, which gain wide popularity in recent years, have replaced hand-crafted map processing methods and tedious manual labor. However, aleatoric uncertainty, known as data-dependent uncertainty, inherent in the drawing/scanning/fading defects of the original map sheets and inadequate contexts when cropping maps into small tiles considering the memory limits of the training process, challenges the model to make correct predictions. As aleatoric uncertainty cannot be reduced even with more training data collected, we argue that complementary spatio-temporal contexts can be helpful. To achieve this, we propose a U-Net-based network that fuses spatio-temporal features with cross-attention transformers (U-SpaTem), aggregating information at a larger spatial range as well as through a temporal sequence of images. Our model achieves a better performance than other state-or-art models that use either temporal or spatial contexts. Compared with pure vision transformers, our model is more lightweight and effective. To the best of our knowledge, leveraging both spatial and temporal contexts have been rarely explored before in the segmentation task. Even though our application is on segmenting historical maps, we believe that the method can be transferred into other fields with similar problems like temporal sequences of satellite images. Our code is freely accessible at https://github.com/chenyizi086/wu.2023.sigspatial.git

Detecting graves in GPR data: assessing the viability of machine learning for the interpretation of graves in B-scan data using medieval Irish case studies.

As commercial archaeogeophysical survey progressively shifts towards large landscape-scale surveys, small features like graves become more difficult to identify and interpret. In order to increase the rate and confidence of grave identification before excavation using geophysical methods, the accuracy and speed of survey outputs and reporting must be improved. The approach taken in this research was first to consider the survey parameters that govern the effectiveness of the four conventional techniques used in commercial archaeogeophysical evaluations (magnetometry, earth resistance, electromagnetic induction and ground-penetrating radar). Subsequently, in respect of ground-penetrating radar (GPR), this research developed machine learning applications to improve the speed and confidence of detecting inhumation graves. The survey parameters research combined established survey guidelines for the UK, Ireland, and Europe to account for local geology, soils and land cover to provide survey guidance for individual sites via a decision-based application linked to GIS database. To develop two machine learning tools for localising and probability scoring grave-like responses in GPR data, convolutional neural networks and transfer learning were used to analyse radargrams of medieval graves and timeslices of modern proxy clandestine graves. Models were c. 93% accurate at labelling images as containing a grave or no grave and c. 96% accurate in labelling and locating potential graves in radargram images. For timeslices, machine learning models achieved 94% classification accuracy. The >90% accuracy of the machine learning models demonstrates the viability of machine-assisted detection of inhumation graves within GPR data. While the expansion of the training dataset would further improve the accuracy of the proposed methods, the current machine-led interpretation methods provide valuable assistance for human-led interpretation until more data becomes available. The survey guidance tool and the two machine learning applications have been packaged into the Reilig web application toolset, which is freely available

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society