Spatial pattern mining (SPM) aims to detect geographic locations or areas that present interesting, nontrivial, and potentially useful patterns. Traditional formulations of point-based SPM tasks are mainly based on true observations, which tend to have limited spatial coverage, availability, and timeliness. While machine learning (ML) has the potential to extend the range of usable data, the uncertainty of model-predicted labels presents new challenges for their usability in the SPM context. This paper formulates the task of ML-enabled SPM using predicted labels by ML models. Given the ever-expanding family of spatial patterns, we consider four widely-adopted patterns – hotspots, co-locations, mixture patterns, and spatial outliers – to scope our study to make the discussion concrete. We develop soft-label versions of SPM algorithms that can directly execute on uncertain predictions generated by ML models. Additionally, we evaluate the ML-enabled SPM results for both categorical and real-valued datasets across a spectrum of prediction quality. The results show that certain spatial patterns such as multinomial scan statistic-based mixture patterns and normal-model-based hotspots can more robustly maintain the detection quality at different error levels, while others such as spatial outliers are more sensitive to incorrect predictions. This provides helpful guidance on using learning-based predictions for SPM.

Fairness related to locations (i.e., “where”) is critical for the use of machine learning in a variety of societal domains involving spatial datasets (e.g., agriculture, disaster response, urban planning). Spatial biases incurred by learning, if left unattended, may cause or exacerbate unfair distribution of resources, social division, spatial disparity, etc. The goal of this work is to develop statistically-robust formulations and model-agnostic learning strategies to understand and promote spatial fairness. The problem is challenging as locations are often from continuous spaces with no well-defined categories (e.g., gender), and statistical conclusions from spatial data are fragile to changes in spatial partitionings and scales. Existing studies in fairness-driven learning have generated valuable insights related to non-spatial factors including race, gender, education level, etc., but research to mitigate location-related biases still remains in its infancy, leaving the main challenges unaddressed. To bridge the gap, we first propose a robust space-as-distribution (SPAD) representation of spatial fairness to reduce statistical sensitivity related to partitionings and scales in continuous space. Furthermore, we propose a new SPAD-based stochastic strategy to efficiently optimize over an extensive distribution of fairness criteria, and a bi-level training framework to enforce fairness via adaptive adjustment of priorities among locations. Finally, we extend this framework with a similarity-based training strategy to improve the computational efficiency. Experiments conducted on two real-world problems, crop monitoring in the US and palm oil plantation mapping in Indonesia, show that SPAD can effectively reduce sensitivity in fairness evaluation and the stochastic bi-level training framework can greatly improve the fairness. Controlled experiments also show that similarity-based acceleration can greatly reduce the training time while keeping the prediction performance and fairness results at the same level.

Airborne LiDAR point clouds record three-dimensional structures of ground surfaces with high precision, and have been widely used to identify geospatial objects, facilitating the understanding of the distribution and changing dynamics of the environment. Detection can be complicated by the complex structures of ground objects and noises in LiDAR point clouds. Related work has explored the use of deep learning techniques such as YOLO in detecting geospatial objects (e.g., building footprints) on both optical imagery and LiDAR point clouds. However, deep networks are data hungry and there are often limited labeled samples available for many geospatial object mapping tasks, making it difficult for the models to generalize to unseen test regions. This paper describes the framework used in the 11th SIGSPATIAL Cup Competition (GIS CUP 2022), which received the top-3 performance. Our framework incorporates domain knowledge to reduce the difficulty of learning and the model's reliance on large training sets. Specifically, we present knowledge-informed feature generation and filtering based on morphological characteristics to improve the generalizability of learned features. Then, we use a deep segmentation backbone (U-Net) with training- and test-time augmentation to generate preliminary candidates for building footprints. Finally, we utilize domain rules (e.g., geometric properties) to regularize and filter the detections to create the final map of building footprints. Experiment results show that the strategies can effectively improve detection results in different landscapes.

As the adoption of machine learning continues to thrive, fairness of the algorithms has become a key factor determining their long-term success and sustainability. Among them, location-based fairness - or spatial fairness - is critical for a variety of essential societal applications that commonly rely on spatial data, including agriculture, disaster response, urban planning, etc. Spatial biases incurred by learning, if left unattended, may cause or exacerbate unfair distribution of resources, spatial disparity, social division, etc. However, very limited understanding has been developed on location-based fairness and bias in machine learning. Compared to traditional fairness-preserving techniques, the spatial consideration introduces two major layers of complication: (1) Space is continuous with no well-defined categories (e.g., categories by race or gender); and (2) Categorizations given by space-partitionings are known to be subject to high statistical sensitivity (e.g., gerrymandering). Under these challenges, we formally explore and demonstrate the fragility of learning methods in the spatial fairness-bias sphere. Specifically, we present a set of techniques that can maneuver the training process towards various targeted fairness-bias outcomes, while maintaining the same level of overall prediction performance (i.e., for "free"). Extensive experiments are carried out on two real-world problems: crop monitoring in the US and palm oil plantation mapping in Indonesia. The results demonstrate the effectiveness of the manipulation algorithms and the importance of explicitly regulating location-based fairness using a diverse set of criteria.

Adversarial examples pose many security threats to convolutional neural networks (CNNs). Most defense algorithms prevent these threats by finding differences between the original images and adversarial examples. However, the found differences do not contain features about the classes, so these defense algorithms can only detect adversarial examples without recovering the correct labels. In this regard, we propose the Adversarial Feature Genome (AFG), a novel type of data that contain both the differences and features about classes. This method is inspired by an observed phenomenon, namely, the Adversarial Feature Separability, where the difference between the feature maps of the original images and adversarial examples becomes larger with deeper layers. On top of that, we further develop an adversarial example recognition framework that detects adversarial examples and can recover the correct labels. In the experiments, the detection and classification of adversarial examples by AFGs has an accuracy of more than 90.01% in various attack scenarios. To the best of our knowledge, our method is the first method that focuses on both attack detecting and recovering. AFG gives a new data-driven perspective to improve the robustness of CNNs.