Spatial and spatiotemporal prediction tasks are common in applications ranging from environmental sciences to archaeology and epidemiology. While sophisticated mathematical frameworks have long been developed in spatial statistics to characterize predictive uncertainties under well-defined mathematical assumptions such as intrinsic stationarity (e.g., Cressie 1993), computational estimation procedures have only been proposed more recently to assess predictive performances of spatial and spatiotemporal prediction models (Brenning 2005, 2012; Pohjankukka, Pahikkala, Nevalainen, and Heikkonen 2017; Roberts, Bahn, Ciuti, Boyce, Elith, Guillera-Arroita, Hauenstein, Lahoz-Monfort, Schröder, Thuiller, Warton, Wintle, Hartig, and Dormann 2017). Although alternatives such as the bootstrap exist since some decades (Efron and Gong 1983; Hand 1997), cross-validation (CV) is a particularly well-established, easy-to-implement algorithm for model assessment of supervised machine-learning models (Efron and Gong 1983, and next section) and model selection (Arlot and Celisse 2010). In its basic form, CV is based on resampling the data without paying attention to any possible dependence structure, which may arise from, e.g., grouped or structured data, or underlying environmental processes inducing some sort of spatial coherence at the landscape scale. In treating dependent observations as independent, or ignoring autocorrelation, CV test samples may in fact be heavily correlated with, or even pseudo-replicates of, the data used for training the model, which introduces a potentially severe bias in assessing the transferability of flexible machine-learning (ML) models.

Robots equipped with situational awareness can help humans efficiently find their lost objects by leveraging spatial and temporal structure. Existing approaches to video and image retrieval do not take into account the unique constraints imposed by a moving camera with a partial view of the environment. We present a Detection-based 3-level hierarchical Association approach, D3A, to create an efficient query-able spatial-temporal representation of unique object instances in an environment. D3A performs online incremental and hierarchical learning to identify keyframes that best represent the unique objects in the environment. These keyframes are learned based on both spatial and temporal features and once identified their corresponding spatial-temporal information is organized in a key-value database. D3A allows for a variety of query patterns such as querying for objects with/without the following: 1) specific attributes, 2) spatial relationships with other objects, and 3) time slices. For a given set of 150 queries, D3A returns a small set of candidate keyframes (which occupy only 0.17% of the total sensory data) with 81.98\% mean accuracy in 11.7 ms. This is 47x faster and 33% more accurate than a baseline that naively stores the object matches (detections) in the database without associating spatial-temporal information.

Humans are expert explorers. Understanding the computational cognitive mechanisms that support this efficiency can advance the study of the human mind and enable more efficient exploration algorithms. We hypothesize that humans explore new environments efficiently by inferring the structure of unobserved spaces using spatial information collected from previously explored spaces. This cognitive process can be modeled computationally using program induction in a Hierarchical Bayesian framework that explicitly reasons about uncertainty with strong spatial priors. Using a new behavioral Map Induction Task, we demonstrate that this computational framework explains human exploration behavior better than non-inductive models and outperforms state-of-the-art planning algorithms when applied to a realistic spatial navigation domain.

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image which is often the case in current literature. Since lane markings are continuous lines, the lanes that are difficult to be accurately detected in the single current image can potentially be better deduced if information from previous frames is incorporated. This study proposes a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple continuous image frames to detect lane markings in the very last current frame. Specifically, the hybrid model integrates the single image feature extraction module with the spatial convolutional neural network (SCNN) embedded for excavating spatial features and relationships in one single image, the spatial-temporal feature integration module with spatial-temporal recurrent neural network (ST-RNN), which can capture the spatial-temporal correlations and time dependencies among image sequences, and the encoder-decoder structure, which makes this image segmentation problem work in an end-to-end supervised learning format. Extensive experiments reveal that the proposed model can effectively handle challenging driving scenes and outperforms available state-of-the-art methods with a large margin.

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public risk assessment. While previous work has made significant efforts to learn traffic temporal dynamics and spatial dependencies, two key limitations exist in current models. First, only the neighboring spatial correlations among adjacent regions are considered in most existing methods, and the global inter-region dependency is ignored. Additionally, these methods fail to encode the complex traffic transition regularities exhibited with time-dependent and multi-resolution in nature. To tackle these challenges, we develop a new traffic prediction framework-Spatial-Temporal Graph Diffusion Network (ST-GDN). In particular, ST-GDN is a hierarchically structured graph neural architecture which learns not only the local region-wise geographical dependencies, but also the spatial semantics from a global perspective. Furthermore, a multi-scale attention network is developed to empower ST-GDN with the capability of capturing multi-level temporal dynamics. Experiments on several real-life traffic datasets demonstrate that ST-GDN outperforms different types of state-of-the-art baselines. Source codes of implementations are available at https://github.com/jill001/ST-GDN.

Apple has been slowly but surely creating a name for itself in the low-code/no-code movement. This July, the Cupertino-based company announced the launch of Trinity AI, a no-code platform for complex spatial datasets. Trinity enables machine learning researchers and non-AI devs to tailor complex spatiotemporal datasets to fit deep learning models. Back in 2019, Apple revealed SwiftUI, a programming language that required much less coding than the Swift language. With the release of Trinity, Apple doubles down on its effort to significantly lower the threshold for non-devs and non-ML devs.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Apple has been slowly but surely creating a name for itself in the low-code/no-code movement. This July, the Cupertino-based company announced the launch of Trinity AI, a no-code platform for complex spatial datasets. Trinity enables machine learning researchers and non-AI devs to tailor complex spatiotemporal datasets to fit deep learning models. Back in 2019, Apple revealed SwiftUI, a programming language that required much less coding than the Swift language.

It is simply looking at where things happen to understand why they happen there. Geospatial Data Science is the discipline that specifically focuses on the spatial component of data science. Spatial Analysis is considered as a core infrastructure of the modern tech industry and is heavily substantiated by the business transactions of world-leading companies such as Uber, Deliveroo, Apple, Google, Intel, and evidently by the motor companies such as Tesla, BMW, and Mercedes. So, these companies are bound to hire more and more Spatial Data Analysts and Geo-Spatial Scientists. Based on these business trends, we've compiled the spatial analysis courses designed by world-class educators to help beginners gain solid foundations of spatial data analysis.

Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and motion planning of autonomous vehicles. This paper proposes a graph-based spatial-temporal convolutional network (GSTCN) to predict future trajectory distributions of all neighbor vehicles using past trajectories. This network tackles the spatial interactions using a graph convolutional network (GCN), and captures the temporal features with a convolutional neural network (CNN). The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Besides, we propose a weighted adjacency matrix to describe the intensities of mutual influence between vehicles, and the ablation study demonstrates the effectiveness of our proposed scheme. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM).Comparisons in three aspects, including prediction errors, model sizes, and inference speeds, show that our network can achieve state-of-the-art performance.

With a total of 67,956 kilometers of railways in 2020 India ranked 4th just behind the United States, China and Russia. While Indian Railways, a statutory body under the Indian Ministry of Railways, manages one of the world's largest rail networks, adjusting for the size of the country reveals that a much smaller portion of the territory is covered in railroads. Using the 2019 official subdistrict boundary data generously provided by superb GIS specialist Justin Elliot Meyers on his rich GitHub page as well as Geofabrik OpenStreetMap data for India, we'll learn how to effortlessly compute the length of railways and land area size for every Indian subdistrict polygon to arrive at railway density, measured as 1 kilometer of railway per 100 square kilometers of land area. And we'll do it programatically in R using 150 lines of code. Once you go through the code, you'll be able to apply it to other spatial lines such as roads or rivers. This code could ultimately inspire you to launch your own projects on, for example, motorway density or river length per population.