A typical human strategy for giving navigation guidance is to sketch route maps based on the environmental layout. Inspired by this, we introduce Sketch map-based visual Navigation (SkeNa), an embodied navigation task in which an agent must reach a goal in an unseen environment using only a hand-drawn sketch map as guidance. To support research for SkeNa, we present a large-scale dataset named SoR, comprising 54k trajectory and sketch map pairs across 71 indoor scenes. In SoR, we introduce two navigation validation sets with varying levels of abstraction in hand-drawn sketches, categorized based on their preservation of spatial scales in the environment, to facilitate future research. To construct SoR, we develop an automated sketch-generation pipeline that efficiently converts floor plans into hand-drawn representations. To solve SkeNa, we propose SkeNavigator, a navigation framework that aligns visual observations with hand-drawn maps to estimate navigation targets. It employs a Ray-based Map Descriptor (RMD) to enhance sketch map valid feature representation using equidistant sampling points and boundary distances. To improve alignment with visual observations, a Dual-Map Aligned Goal Predictor (DAGP) leverages the correspondence between sketch map features and on-site constructed exploration map features to predict goal position and guide navigation. SkeNavigator outperforms prior floor plan navigation methods by a large margin, improving SPL on the high-abstract validation set by 105% relatively. Our code and dataset will be released.

Spatial-query-by-sketch is an intuitive tool to explore human spatial knowledge about geographic environments and to support communication with scene database queries. However, traditional sketch-based spatial search methods perform insufficiently due to their inability to find hidden multi-scale map features from mental sketches. In this research, we propose a deep convolutional neural network, namely Deep Spatial Scene Network (DeepSSN), to better assess the spatial scene similarity. In DeepSSN, a triplet loss function is designed as a comprehensive distance metric to support the similarity assessment. A positive and negative example mining strategy using qualitative constraint networks in spatial reasoning is designed to ensure a consistently increasing distinction of triplets during the training process. Moreover, we develop a prototype spatial scene search system using the proposed DeepSSN, in which the users input spatial query via sketch maps and the system can automatically augment the sketch training data. The proposed model is validated using multi-source conflated map data including 131,300 labeled scene samples after data augmentation. The empirical results demonstrate that the DeepSSN outperforms baseline methods including k-nearest-neighbors, multilayer perceptron, AlexNet, DenseNet, and ResNet using mean reciprocal rank and precision metrics. This research advances geographic information retrieval studies by introducing a novel deep learning method tailored to spatial scene queries.

Sketch maps are an important spatial representation used in many geospatial-reasoning tasks. This article describes techniques we have developed that enable software to perform humanlike reasoning about sketch maps. We illustrate the utility of these techniques in the context of nuSketch Battlespace, a research system that has been successfully used in a variety of experiments. After an overview of the nuSketch approach and nuSketch Battlespace, we outline the representations of glyphs and sketches and the nuSketch spatial reasoning architecture. We describe the use of qualitative topology and Voronoi diagrams to construct spatial representations, and explain how these facilities are combined with analogical reasoning to provide a simple form of enemy intent hypothesis generation.

Sketch maps are an important spatial representation used in many geospatial-reasoning tasks. This article describes techniques we have developed that enable software to perform humanlike reasoning about sketch maps. We illustrate the utility of these techniques in the context of nuSketch Battlespace, a research system that has been successfully used in a variety of experiments. After an overview of the nuSketch approach and nuSketch Battlespace, we outline the representations of glyphs and sketches and the nuSketch spatial reasoning architecture.

Sketch maps are an important spatial representation used in many geospatial-reasoning tasks. This article describes techniques we have developed that enable software to perform humanlike reasoning about sketch maps. We illustrate the utility of these techniques in the context of nuSketch Battlespace, a research system that has been successfully used in a variety of experiments. After an overview of the nuSketch approach and nuSketch Battlespace, we outline the representations of glyphs and sketches and the nuSketch spatial reasoning architecture. We describe the use of qualitative topology and Voronoi diagrams to construct spatial representations, and explain how these facilities are combined with analogical reasoning to provide a simple form of enemy intent hypothesis generation.

We are pleased to publish this special selection of papers from the 2003 Innovative Applications of Artificial Intelligence Conference (IAAI-03). IAAI seeks out applications of artificial intelligence that either demonstrate new technology or use previously known technology in innovative ways. IAAI particularly seeks out examples of deployments of AI technology that tackle the problems of demonstrating value and planning for long-term deployment. The five articles we have selected for this special issue are extended versions of papers that appeared in the conference. Two of the articles are deployed applications that have already demonstrated practical value. The remaining three articles are particularly innovative emerging applications. We will briefly outline each of them.