People recognize and characterize patterns to understand the world. Spatial data exhibit distinctive characteristics that render most aspatial recognition and matching methods unsuitable or inefficient. In past decades, a plethora of methods have been developed for spatial pattern recognition and matching to account for these spatial characteristics. This entry first focuses on the methods of spatial pattern recognition, including an overview of the basic concepts and common types. Methods for spatial pattern matching are then introduced. An example scenario of the distribution of tree species in the Arbuckle Mountains of south-central Oklahoma illustrates covered concepts. The entry concludes with brief remarks on continuing challenges and future directions in spatial pattern recognition and matching in the Big Data and artificial intelligence era.
Machine learning approaches are increasingly used across numerous applications in order to learn from data and generate new knowledge discoveries, advance scientific studies and support automated decision making. In this knowledge entry, the fundamentals of Machine Learning (ML) are introduced, focusing on how feature spaces, models and algorithms are being developed and applied in geospatial studies. An example of a ML workflow for supervised/unsupervised learning is also introduced. The main challenges in ML approaches and our vision for future work are discussed at the end.
Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.
Feature extraction in satellite imagery is fundamental to the goal of gathering timely, large-area geospatial information relevant to GIScience research and beyond. There are two approaches in remote sensing to feature extraction. One approach involves identifying phenomena in imagery to be reduced into map form (typically features such as categories or land surface elements). A second approach is to enhance and extract specific bands of imagery and transform them in order to provide a reduced set of inputs or predictors to a model (e.g., a vegetation index). This section focuses only on the former. Extraction of features is performed using a conceptualization of the study site known as a scene model, and the combination of ground reference information and appropriately chosen satellite data. Features can be represented in maps as discrete pixels, polygons or fuzzy membership surfaces, and machine learning algorithms have emerged as the most reliable and effective approaches to feature extraction in the last decade. There are five key steps to performing effective feature extraction: (1) developing a scene model to determine the appropriate scales of information required for a project; (2) ground reference data collection to support the calibration and validation process; (3) selection of appropriate satellite image data, and this can include ancillary data such as digital elevation models; (4) application of a feature extraction algorithm that can best distinguish the feature(s) of interest from background features and produce a map product that is logically consistent; and (5) assessment of map accuracy using validation data to determine the quality of the product for various uses.
This topic entry outlines the recent advances and the transformative integration of artificial intelligence with geospatial science, focusing on modern programming libraries and infrastructures for raster data analysis. We explain how the convergence of AI and geospatial technologies has revolutionized Earth observation analysis through advanced machine learning algorithms, computer vision techniques, and modern infrastructures. The entry highlights the evolution of raster data analysis, from traditional processing methods to sophisticated AI-driven approaches that enable automated feature extraction and object detection at an unprecedented scale. We discuss key developments in programming infrastructure, including Python-based frameworks, GPU-accelerated computing solutions, and cloud-native platforms that have emerged to address the challenges of big Earth data. Special attention is given to dataframe-based analysis approaches and distributed computing frameworks that have enhanced the processing capabilities of large-scale geospatial datasets. We also introduce the cloud computing platforms such as Google Earth Engine, AWS, and Microsoft’s Planetary Computer, which have democratized access to Earth observation analysis and empowered researchers to conduct impactful research using geospatial data. By presenting this comprehensive overview, we provide insights into current capabilities and future directions in the field, emphasizing the continuing evolution of GeoAI technologies and their impact on environmental monitoring, urban planning, resource management, etc.