Dearth of labeled data for training supervised deep learning models for real-world applications of computer vision plagues the large-scale deployment of machine learning models in many domains; including problems in geospatial analysis and remote sensing. As an example, detecting infrequent events or changes like road closures, road blocks, junctions changing to roundabouts, temporary turn restrictions, etc. are critical to keep a geospatial mapping service up-to-date in real-time and can significantly improve user experience and above all, user safety. Obtaining labels to train supervised models to detect infrequent mobility change events is expensive in time and money for a multitude of reasons [13]. An inexpensive solution is synthetic generation of training data and labels based on user-provided conditional inputs that can be easily manipulated. In [13], we propose a novel deep conditional generative model that can synthetically generate various types of semantically rich, imagelike representations of GPS trajectory data (e.g., CRM, HCRM discussed below) by conditioning simultaneously on pixel-level (e.g, road network) and feature-level (e.g., desired observation time interval) conditional inputs. Detection (e.g., pedestrian crosswalks, road centerlines) and classification (e.g, landcover, vegetation) of geospatial features are routinely cast as canonical tasks in computer vision such as object detection, semantic segmentation, instance segmentation, etc. Analogously, detecting locations affected by the aforementioned changes in mobility can be cast as a semantic segmentation task from image-like representations of privacy-preserving GPS trajectory datasets [13]. Consider a raster representation of the earth's surface created with zoom-24 tiles [3]. Any contiguous set of n n tiles can be considered as an n n image whose pixels are the associated zoom-24 tiles. A count-based raster map (CRM) is a single-channel image-like representation where the value of each pixel is the number of GPS trace occurrences in the zoom-24 tile corresponding to the pixel, counted over all trajectories during an observation time interval, τ t.

Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Conversely, obtaining enough training data for many applications is financially prohibitive or may be infeasible, especially when the application involves modeling rare or extreme events. Synthetically generating data (and labels) using a generative model that can sample from a target distribution and exploit the multi-scale nature of images can be an inexpensive solution to address scarcity of labeled data. Towards this goal, we present a deep conditional generative model, called VAE-Info-cGAN, that combines a Variational Autoencoder (VAE) with a conditional Information Maximizing Generative Adversarial Network (InfoGAN), for synthesizing semantically rich images simultaneously conditioned on a pixel-level condition (PLC) and a macroscopic feature-level condition (FLC). Dimensionally, the PLC can only vary in the channel dimension from the synthesized image and is meant to be a task-specific input. The FLC is modeled as an attribute vector in the latent space of the generated image which controls the contributions of various characteristic attributes germane to the target distribution. An interpretation of the attribute vector to systematically generate synthetic images by varying a chosen binary macroscopic feature is explored. Experiments on a GPS trajectories dataset show that the proposed model can accurately generate various forms of spatio-temporal aggregates across different geographic locations while conditioned only on a raster representation of the road network. The primary intended application of the VAE-Info-cGAN is synthetic data (and label) generation for targeted data augmentation for computer vision-based modeling of problems relevant to geospatial analysis and remote sensing.