Huanfeng Shen, Wuhan University ([email protected]), Jane Liu, University of Toronto ([email protected]), Wenping Yuan, Sun Yat-Sen University ([email protected]), Yongguang Zhang, Nanjing University ([email protected]), Holly Croft, University of Sheffield ([email protected]), Xiaobin Guan, Wuhan University ([email protected]). The dramatic increase in anthropogenic carbon emissions over the last five decades has already led to substantial damage to our environment, including increases in extreme weatherevents, loss of biodiversity, and a rise in sea level. Carbon neutrality, i.e., net-zero anthropogenic carbon emissions, is necessary to ensure the sustainable future of human beings, and hundreds of countries have pledged to achieve this goal by mid-century. Remote sensing techniques can acquire frequent observations of the Earth with various temporal and spatial resolutions, and provide substantial information for carbon emission monitoring and carbon cycle modeling. Remote sensing observations not only can be directly applied to retrieve the atmospheric concentrations of greenhouse gases (e.g., CO2, CO, CH4, CFCs, O3, et al.), but also can be employed to investigate the carbon budget of natural ecosystems.

Terrain analysis in Real-Time Strategy games is a necessary step to allow spacial reasoning. The goal of terrain analysis is to gather and process data about the map topology and properties to have a qualitative spatial representation. On StarCraft games, all previous works on terrain analysis propose a crisp analysis based on connected component detection, Voronoi diagram computation and pruning, and region merging. Those methods have been implemented as game-specific libraries, and they can only offer the same kind of analysis for all maps and all users. In this paper, we propose a way to consider terrain analysis as a combinatorial optimization problem. Our method allows different kinds of analysis by changing constraints or the objective function in the problem model. We also present a library, Taunt, implementing our method and able to handle both StarCraft 1 and StarCraft 2 maps. This makes our library a universal tool for StarCraft bots with different spatial representation needs. We believe our library unlocks the possibility to have real adaptive AIs playing StarCraft, and can be the starting point of a new wave of bots.

Crime has become a major concern in many cities, which calls for the rising demand for timely predicting citywide crime occurrence. Accurate crime prediction results are vital for the beforehand decision-making of government to alleviate the increasing concern about the public safety. While many efforts have been devoted to proposing various spatial-temporal forecasting techniques to explore dependence across locations and time periods, most of them follow a supervised learning manner, which limits their spatial-temporal representation ability on sparse crime data. Inspired by the recent success in self-supervised learning, this work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework (ST-HSL) to tackle the label scarcity issue in crime prediction. Specifically, we propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space. Furthermore, we design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination. We perform extensive experiments on two real-life crime datasets. Evaluation results show that our ST-HSL significantly outperforms state-of-the-art baselines. Further analysis provides insights into the superiority of our ST-HSL method in the representation of spatial-temporal crime patterns. The implementation code is available at https://github.com/LZH-YS1998/STHSL.

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is that the data will be collected with high spatial and temporal diversity to best characterize a building and the occupant's preferences. But in reality, leaving the occupant to respond in an ad-hoc or fixed interval way creates unneeded survey fatigue and redundant data. This paper outlines a scenario-based (virtual experiment) method for optimizing data sampling using a smartwatch to achieve comparable accuracy in a personal thermal preference model with fewer data. This method uses BIM-extracted spatial data and Graph Neural Network-based (GNN) modeling to find regions of similar comfort preference to identify the best scenarios for triggering the occupant to give feedback. This method is compared to two baseline scenarios that use conventional zoning and a generic 4x4 square meter grid method from two field-based data sets. The results show that the proposed Build2Vec method has an 18-23\% higher overall sampling quality than the spaces-based and square-grid-based sampling methods. The Build2Vec method also performs similar to the baselines when removing redundant occupant feedback points but with better scalability potential.

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

Geospatial Data Analyses & Remote Sensing: 5 Classes in 1 Do you need to design a GIS map or satellite-imagery based map for your Remote Sensing or GIS project but you don't know how to do this? Have you heard about Remote Sensing object-based image analysis and machine learning or maybe QGIS or Google Earth Engine but did not know where to start with such analyses? Do you find Remote Sensing and GIS manuals too not practical and looking for a course that takes you by hand, teach you all the concepts, and get you started on a real-life GIS mapping project? I'm very excited that you found my Practical Geospatial Masterclass on Geospatial Data Analyses & Remote Sensing. This course provides and information that is usually delivered in 4 separate Geospatial Data Analyses & Remote Sensing courses, and thus you with learning all the necessary information to start and advance with Geospatial analysis and includes more than 9 hours of video content, plenty of practical analysis, and downloadable materials.

Become proficient in satellite remote sensing, spatial analysis principles, methods, applications, sensors, and GIS! Get this course for only 9.99. Do you find that other remote sensing courses are too short and vague, and do not prepare you for real world problems? Are you looking for a course that goes IN-DEPTH and teaches you all the fundamentals of remote sensing? My course provides a solid foundation to carry out practical, real life remote sensing spatial data analysis and gives you the techniques and knowledge to tackle a variety of geological and environmental problems. This course provides an introduction to remote sensing - the acquisition of information about the earth from a distance, typically via airborne and spaceborne sensors.

Gaussian Simulation of Spatial Data Using R Richard E. Plant This post is a condensed version of an Additional Topic to accompany Spatial Data Analysis in Ecology and Agriculture using R, Second Edition. The full version together with the data and R code can be found in the Additional Topics section of the book’s website, … Continue reading Gaussian Simulation of Spatial Data Using RGaussian Simulation of Spatial Data Using R was first posted on March 21, 2022 at 2:38 pm.

Learn how to visualize spatial data in maps and charts. Manipulate, clean and transform data. Welcome to the'Spatial Data Visualization and Machine Learning in Python' course. In this course we will be building a spatial data analytics dashboard using bokeh and python. We will be visualizing our data in a variety of bokeh charts, which we will explore in depth.

Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA has been a rapidly growing field which has found success in a wide range of applications, including signal processing, neuroscience and network analysis. In these applications, the online detection of changes is of crucial importance, but this can be highly challenging since such changes often occur in a low-dimensional embedding within high-dimensional data streams. We thus propose a new method, called PERsistence diagram-based ChangE-PoinT detection (PERCEPT), which leverages the learned topological structure from TDA to sequentially detect changes. PERCEPT follows two key steps: it first learns the embedded topology as a point cloud via persistence diagrams, then applies a non-parametric monitoring approach for detecting changes in the resulting point cloud distributions. This yields a non-parametric, topology-aware framework which can efficiently detect online changes from high-dimensional data streams. We investigate the effectiveness of PERCEPT over existing methods in a suite of numerical experiments where the data streams have an embedded topological structure. We then demonstrate the usefulness of PERCEPT in two applications in solar flare monitoring and human gesture detection.