In this article a few of the qualitative spatio-temporal knowledge representation techniques developed by the constraint reasoning community within artificial intelligence are reviewed. The objective is to provide a broad exposure to any other interested group who may utilize these representations. The author has a particular interest in applying these calculi (in a broad sense) in topological data analysis, as these schemes are highly qualitative in nature.

We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment to a goal position, and then identify in the observed image a location described in natural language to find a hidden object. The data contains 9,326 examples of English instructions and spatial descriptions paired with demonstrations. We perform qualitative linguistic analysis, and show that the data displays richer use of spatial reasoning compared to related resources. Empirical analysis shows the data presents an open challenge to existing methods.

Understanding and accurately predicting within-field spatial variability of crop yield play a key role in site-specific management of crop inputs such as irrigation water and fertilizer for optimized crop production. However, such a task is challenged by the complex interaction between crop growth and environmental and managerial factors, such as climate, soil conditions, tillage, and irrigation. In this paper, we present a novel Spatial-temporal Multi-Task Learning algorithms for within-field crop yield prediction in west Texas from 2001 to 2003. This algorithm integrates multiple heterogeneous data sources to learn different features simultaneously, and to aggregate spatial-temporal features by introducing a weighted regularizer to the loss functions. Our comprehensive experimental results consistently outperform the results of other conventional methods, and suggest a promising approach, which improves the landscape of crop prediction research fields.

UNSUPERVISED DEEP CLUSTERING FOR SOURCE SEPARATION: DIRECT LEARNING FROM MIXTURES USING SPATIAL INFORMATION Efthymios Tzinis ] Shrikant Venkataramani ] Paris Smaragdis ][ ] University of Illinois at Urbana-Champaign, Department of Computer Science [ Adobe Research ABSTRACT We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We show that using such a training process we can obtain separation performance that is as good as making use of ground truth separation information. Once trained, this system is capable of performing sound separation on monophonic inputs, despite having learned how to do so using multi-channel recordings. Index Terms -- Deep clustering, source separation, unsupervised learning 1. INTRODUCTION A central problem when designing source separation systems is that of defining what constitutes a source.

Big Data is an evolving term that refers to the collection and analysis of large volumes of data to identify patterns and trends, then make predictions about future outcomes. In agriculture, Big Data is not only the quantity of data--it is also an algorithm of interaction with many variables applied to research like agronomy, breeding, farming systems modeling, strategic foresight, nutrition, health, and socio-economy. "What we mean by Big Data is the linked data," says Dr. Jawoo Koo, a Spatial Data and Analytics theme leader and senior research fellow for the International Food Policy Research Institute (IFPRI). "For us, Big Data is data from multiple disciplines that links together to give us a better understanding of how agriculture works." Koo is co-pioneer of the CGIAR Platform for Big Data in Agriculture and Global Coordinator for the CGIAR Consortium for Spatial Information with extensive work experience in data analysis and crop modeling.

Abstract--The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. IGITAL satellite data with different spatial and spectral resolution are available for almost every locality on the Earth's land surface [1]-[5]. This enables the procurement of detailed information from surficial features and processes at different scales. Linear features are considered as one of the most important surficial features in different fields of study [6]-[8]. R. Scalzo is with the Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia (email: richard.scalzo@sydney.edu.au). Linear features represent the expression of some degree of linearity of a single or diverse grouping of both natural and cultural features [9], [10].

The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving. The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants. One of the most fundamental perceptual notions, space, cannot be an exception to this requirement. In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot's sensorimotor flow. We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment. The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs. In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent's exteroceptors. We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina. The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina.

Despite its omnipresence in robotics application, the nature of spatial knowledge and the mechanisms that underlie its emergence in autonomous agents are still poorly understood. Recent theoretical work suggests that the concept of space can be grounded by capturing invariants that space's structure induces in an agent's raw sensorimotor experience. Moreover, it is hypothesized that capturing these invariants is beneficial for a naive agent trying to predict its sensorimotor experience. Under certain exploratory conditions, spatial representations should thus emerge as a byproduct of learning to predict. We propose a simple sensorimotor predictive scheme, apply it to different agents and types of exploration, and evaluate the pertinence of this hypothesis. We show that a naive agent can capture the topology and metric regularity of its spatial configuration without any a priori knowledge, nor extraneous supervision. Space appears to be a pervasive concept in our perception of the world, and as such plays a central role in most artificial perception systems, in particular in computer vision and robotics applications. Yet its fundamental nature and the mechanisms that could lead to its emergence in an artificial system still remain poorly understood (Kant, 1998; Poincaré, 1895; Nicod, 1924). In most cases, the problem is circumvented by implementing prior knowledge in the system regarding the structure of space, and how motor and sensory information convey spatial properties (for instance through a kinematics model (Siciliano & Khatib, 2016), or a sensor model (Cadena et al., 2016)). In more recent years, and with the developments of machine learning techniques, approaches with less handengineered priors developed to solve spatial tasks ((Kahn et al., 2017; Quillen et al., 2018; Levine et al., 2018; Smolyanskiy et al., 2017) to name a few). However they tend to rule out the specificity of spatial experiences in favor of a global assessment of the agent's performance, leaving the question of the origin and structure of spatial knowledge largely open.

Machine learning methods such as convolutional neural networks (CNNs) are becoming an integral part of scientific research in many disciplines, spatial vector data often fail to be analyzed using these powerful learning methods because of its irregularities. With the aid of graph Fourier transform and convolution theorem, it is possible to convert the convolution as a point-wise product in Fourier domain and construct a learning architecture of CNN on graph for the analysis task of irregular spatial data. In this study, we used the classification task of building patterns as a case study to test this method, and experiments showed that this method has achieved outstanding results in identifying regular and irregular patterns, and has significantly improved in comparing with other methods.

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.