From earth system sciences to climate modeling and ecology, many of the greatest empirical modeling challenges are geographic in nature. As these processes are characterized by spatial dynamics, we can exploit their autoregressive nature to inform learning algorithms. We introduce SXL, a method for learning with geospatial data using explicitly spatial auxiliary tasks. We embed the local Moran's I, a well-established measure of local spatial autocorrelation, into the training process, "nudging" the model to learn the direction and magnitude of local autoregressive effects in parallel with the primary task. Further, we propose an expansion of Moran's I to multiple resolutions to capture effects at different spatial granularities and over varying distance scales. We show the superiority of this method for training deep neural networks using experiments with real-world geospatial data in both generative and predictive modeling tasks. Our approach can be used with arbitrary network architectures and, in our experiments, consistently improves their performance. We also outperform appropriate, domain-specific interpolation benchmarks. Our work highlights how integrating the geographic information sciences and spatial statistics into machine learning models can address the specific challenges of spatial data.

Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extensive set of features from different perspectives and explanations. Our study aims to build data-driven models for predicting future crime occurrences. In this paper, we propose the use of streetlight infrastructure and Foursquare data along with demographic characteristics for improving future crime incident prediction. We evaluate the classification performance based on various feature combinations as well as with the baseline model. Our proposed model was tested on each smallest geographic region in Halifax, Canada. Our findings demonstrate the effectiveness of integrating diverse sources of data to gain satisfactory classification performance.

Ride-hailing demand prediction is an important prediction task in traffic prediction. An accurate prediction model can help the platform pre-allocate resources in advance to improve vehicle utilization and reduce the wait-time. This task is challenging due to the complicated spatial-temporal relationships among regions. Most existing methods mainly focus on Euclidean correlations among regions. Though there are some methods that use Graph Convolutional Networks (GCN) to capture the non-Euclidean pair-wise correlations, they only rely on the static topological structure among regions. Besides, they only consider fixed graph structures at different time intervals. In this paper, we propose a novel deep learning method called Spatial-Temporal Dynamic Graph Attention Network (STDGAT) to predict the taxi demand of multiple connected regions in the near future. The method uses Graph Attention Network (GAT), which achieves the adaptive allocation of weights for other regions, to capture the spatial information. Furthermore, we implement a Dynamic Graph Attention mode to capture the different spatial relationships at different time intervals based on the actual commuting relationships. Extensive experiments are conducted on a real-world large scale ride-hailing demand dataset, the results demonstrate the superiority of our method over existing methods.

Topological Data Analysis (TDA) provides novel approaches that allow us to analyze the geometrical shapes and topological structures of a dataset. As one important application, TDA can be used for data visualization and dimension reduction. We follow the framework of circular coordinate representation, which allows us to perform dimension reduction and visualization for high-dimensional datasets on a torus using persistent cohomology. In this paper, we propose a method to adapt the circular coordinate framework to take into account sparsity in high-dimensional applications. We use a generalized penalty function instead of an $L_{2}$ penalty in the traditional circular coordinate algorithm. We provide simulation experiments and real data analysis to support our claim that circular coordinates with generalized penalty will accommodate the sparsity in high-dimensional datasets under different sampling schemes while preserving the topological structures.

Collision detection is one of the most challenging tasks for Unmanned Aerial Vehicles (UAVs), especially for small or micro UAVs with limited computational power. In nature, fly insects with compact and simple visual systems demonstrate the amazing ability to navigating and avoid collision in a complex environment. A good example of this is locusts. Locusts avoid collision in a dense swarm relying on an identified vision neuron called Lobula Giant Movement Detector (LGMD) which has been modelled and applied on ground robots and vehicles. LGMD as a fly insect's visual neuron, is an ideal model for UAV collision detection. However, the existing models are inadequate in coping with complex visual challenges unique for UAVs. In this paper, we proposed a new LGMD model for flying robots considering distributed spatial-temporal computing for both excitation and inhibition to enhance the looming selectivity in flying scenes. The proposed model integrated recent discovered presynaptic connection types in biological LGMD neuron into a spatial-temporal filter with linear distributed interconnection. Systematic experiments containing quadcopter's first person view (FPV) flight videos demonstrated that the proposed distributed presynaptic structure can dramatically enhance LGMD's looming selectivity especially in complex flying UAV applications.

The Intelligent GeoSolutions (IGS) team at the University of Maine's Center for Research on Sustainable Forests (CRSF) has released a free interactive mapping tool, the Forest Ecosystem Status a … Trends (ForEST) app, to provide online decision support to private and public forest managers, natural resource agencies, conservation organizations and other stakeholders. With the current outbreak of eastern spruce budworm expanding south from Quebec, up-to-date information about resource conditions and near-term risk are needed to coordinate mitigation actions in response to the outbreak and related market conditions. The ForEST app is the culmination of three years of research and software development by the IGS team in partnership with UMaine's Advanced Computing Group. The interdisciplinary project supported two graduate students in the School of Computing and Information Science, each of whom served as lead developer, as well as undergraduate computer science students who worked as team programmers. The interactive web interface is designed to provide near real-time information about changing forest landscape conditions resulting from the spruce budworm outbreak and ongoing management.

The amount and size of spatiotemporal data sets from different domains have been rapidly increasing in the last years, which demands the development of robust and fast methods to analyze and extract information from them. In this paper, we propose a network-based model for spatiotemporal data analysis called chronnet. It consists of dividing a geometrical space into grid cells represented by nodes connected chronologically. The main goal of this model is to represent consecutive recurrent events between cells with strong links in the network. This representation permits the use of network science and graphing mining tools to extract information from spatiotemporal data. The chronnet construction process is fast, which makes it suitable for large data sets. In this paper, we describe how to use our model considering artificial and real data. For this purpose, we propose an artificial spatiotemporal data set generator to show how chronnets capture not just simple statistics, but also frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Additionally, we analyze a real-world data set composed of global fire detections, in which we describe the frequency of fire events, outlier fire detections, and the seasonal activity, using a single chronnet.

In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the verbal communication and the interaction between robotic units and/or humans, reliable and robust systems w.r.t. noise and processing results are needed. This work builds a foundation to address this task. By using a continuous representation of spatial perception in interiors learned from trajectory data, our approach clusters movement in dependency to its spatial context. We propose an unsupervised learning approach based on a neural autoencoding that learns semantically meaningful continuous encodings of spatio-temporal trajectory data. This learned encoding can be used to form prototypical representations. We present promising results that clear the path for future applications.

Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has been measured in only few studies. In this work, we focus on spatial reasoning and explore the spatial understanding of neural models. First, we describe the following two spatial reasoning IQ tests: rotation and shape composition. Using well-defined rules, we constructed datasets that consist of various complexity levels. We designed a variety of experiments in terms of generalization, and evaluated six different baseline models on the newly generated datasets. We provide an analysis of the results and factors that affect the generalization abilities of models. Also, we analyze how neural models solve spatial reasoning tests with visual aids. Our findings would provide valuable insights into understanding a machine and the difference between a machine and human.

The relative spatial layout of a human and an object is an important cue for determining how they interact. However, until now, spatial layout has been used just as side-information for detecting human-object interactions (HOIs). In this paper, we present a method for exploiting this spatial layout information for detecting HOIs in images. The proposed method consists of a layout module which primes a visual module to predict the type of interaction between a human and an object. The visual and layout modules share information through lateral connections at several stages. The model uses predictions from the layout module as a prior to the visual module and the prediction from the visual module is given as the final output. It also incorporates semantic information about the object using word2vec vectors. The proposed model reaches an mAP of 24.79% for HICO-Det dataset which is about 2.8% absolute points higher than the current state-of-the-art.