Emerging advances from artificial intelligence, hardware accelerators, and data processing architectures continue to reach the geospatial information sciences, with a transformative impact in many societal challenges. Recent breakthroughs in deep learning have brought forward an automated capability to learn hierarchical representational features from massive and complex data, including text, images, and videos. In tandem, rapid innovations in sensing technologies are supporting the collection of geospatial data in even higher resolution and throughput, supporting the observation, mapping, and analysis of different events/phenomena over the earth's surface with unprecedented detail. Combined, these developments are offering potential for breakthroughs in geographic knowledge discovery, impacting decision making in areas such as humanitarian mapping, intelligent transport systems, urban expansion analysis, health data analysis and epidemiology, the study of climate change, handling natural disasters, and the general monitoring of the Earth's surface.
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Cartography in GeoAI: Emerging Themes and Research Challenges
The emergence of prompt-driven artificial intelligence (AI) techniques for the rapid creation and iterative ideation of text, images, and code has disrupted the trajectory of science, technology, and society. Geospatial AI (GeoAI) aims to develop ...
GeoVeX: Geospatial Vectors with Hexagonal Convolutional Autoencoders
We introduce a new geospatial representation model called GeoVeX to learn global vectors for all geographical locations on Earth land cover. GeoVeX is built on a novel model architecture named Hexagonal Convolutional Autoencoders (HCAE) combined with a ...
Self-Attention Driven Decoder for SAR Image-based Semantic Flood Zone Segmentation
Floods are destructive natural calamities that endanger people's lives, infrastructure, and the environment. Flood detection that is timely and accurate can help with disaster management and save lives. Flood semantic segmentation from remote sensing ...
A Detailed Analysis on the Use of General-purpose Vision Transformers for Remote Sensing Image Segmentation
Image segmentation is currently a hot topic in the context of Earth observation through remote sensing. Recent research has advanced many new models designed specifically for remote sensing image segmentation, often with sophisticated architectures and ...
Contrastive Pretraining for Railway Detection: Unveiling Historical Maps with Transformers
Detecting railways from historical maps is challenging due to their infrequent representation in a map sheet and their visual similarity with roads. Basically, both railways and roads are symbolised as two parallel black lines, with slight differences ...
Anomaly Detection for Population Dynamics using Autoencoder Leveraging Periodic Residual Component in Disaster Situations
It is important for local governments to grasp the evacuation situation upon the occurrence of a large-scale disaster because administrative support can be provided, such as opening temporary accommodation facilities or distributing relief supplies. ...
SRAI: Towards Standardization of Geospatial AI
Spatial Representations for Artificial Intelligence (srai) is a Python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model using ...
On Threshold Correlation with Application to Studying the Relationship of Temperature and Relative Humidity
Finding thresholds for continuous variables is an important problem in many applications. For example, when studying the relationship of air pollution and lung diseases it is important to obtain findings such as: "PM2.5 concentrations above 40μg/m3 are ...
FLEE-GNN: A Federated Learning System for Edge-Enhanced Graph Neural Network in Analyzing Geospatial Resilience of Multicommodity Food Flows
- Yuxiao Qu,
- Jinmeng Rao,
- Song Gao,
- Qianheng Zhang,
- Wei-Lun Chao,
- Yu Su,
- Michelle Miller,
- Alfonso Morales,
- Patrick R. Huber
Understanding and measuring the resilience of food supply networks is a global imperative to tackle increasing food insecurity. However, the complexity of these networks, with their multidimensional interactions and decisions, presents significant ...
Signal Separation in Global, Temporal Gravity Data
Satellite gravity data such as provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-up mission contain valuable information on all geophysical processes that involve a mass redistribution in the Earth system. However, as ...
Reimagining standardization and geospatial interoperability in today's GeoAI culture
Integrating Geospatial Artificial Intelligence (GeoAI) into our technological landscape has revolutionized our capacity to understand and engage with the world. However, the burgeoning adoption of GeoAI applications has emphasized the priority of data, ...
Towards Understanding the Geospatial Skills of ChatGPT: Taking a Geographic Information Systems (GIS) Exam
This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their ...
Plane Segmentation in Outdoor Imagery: Unsupervised Training Using Synthetic Data
Plane segmentation in monocular images plays an important role in understanding the geometry of a 3D environment. However, most previous plane segmentation work focuses mostly on indoor environments since it is hard to create precise planar annotations ...
Assessment of a new GeoAI foundation model for flood inundation mapping
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem solving and geographic knowledge discovery, because of their potential to ...
Automated Multi-class Crater Segmentation in Mars Orbital Images
Crater mapping and counting are critical analyses in many planetary science investigations, as the size-frequency distribution of impact craters can be used to measure the age of a planet's surface and interpret its geologic history. Crater counting is ...
Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips ...
Index Terms
- Proceedings of the 6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
GeoAI '19 | 25 | 17 | 68% |
Overall | 25 | 17 | 68% |