Map Generation from Large Scale Incomplete and Inaccurate Data Labels (2005.10053v1)

Abstract: Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.

• The paper introduces an asynchronous distributed SGD framework that achieves near-linear scalability with a 14.7x speed-up on a GPU cluster.
• The paper leverages modified CNN architectures, including U-Net and FW-CycleGAN, to enhance map accuracy by emphasizing key features like roads and buildings.
• The paper demonstrates the feasibility of generating precise maps from imperfect data, highlighting practical applications in routing, regulatory compliance, and disaster management.

An Analysis of Map Generation from Large Scale Incomplete and Inaccurate Data Labels

This paper addresses the challenges of automated map generation from high-resolution aerial images, particularly when training data is incomplete or inaccurately labeled. The authors leverage publicly available datasets such as NAIP and OSM to generate maps covering the contiguous United States (CONUS) using deep learning algorithms optimized for scalability. The effort to scale this task required the implementation of an asynchronous distributed stochastic parallel gradient descent algorithm capable of distributing computational tasks across a GPU cluster with near-linear speed-up.

The research builds upon two main CNN architectures, the U-Net and the CycleGAN, with modifications aimed at enhancing performance given the characteristics of available data. A particular innovation presented in the paper is the Feature-Weighted CycleGAN (FW-CycleGAN), which incorporates a feature-weighted cycle consistency loss to emphasize important features like roads and buildings, improving detection capabilities.

Training Data and Methodology

The paper notes the limitations of existing datasets like OSM, which, while freely available, are unevenly labeled due to their community-sourced nature. The authors have addressed these challenges by proposing a training framework that incrementally refines map accuracy. The geographic heterogeneity highlighted necessitates diverse training data, as models trained on one region (such as the architectural differences between the U.S. and China, or even between different states within the U.S.) showed poor transferability.

To create a consistent training environment, IBM’s PAIRS platform was employed for data ingestion, aligning both NAIP (for aerial imagery) and rasterized OSM maps to ensure pixel-level consistency. This addresses issues such as varied geo-projections and maintains uniform data resolution.

Results and Evaluation

The research demonstrated that U-Net outperforms CycleGAN in scenarios where feature extraction precision is crucial, which aligns with previous challenges in similar domains like SpaceNet. Yet, the introduction of FW-CycleGAN brought significant improvements to CycleGAN’s performance, indicating the importance of feature emphasis in adversarial training contexts. In general, the U-Net model scored higher in recall and precision tests, showcasing its suitability for the task when accurate data is available.

For training scalability, the authors implemented a Decentralized Parallel Stochastic Gradient Descent (DP-SGD) approach, demonstrating a speed-up of 14.7 times with 16 GPUs—a result that signifies the feasibility of mapping large areas at reduced computation times.

Implications and Future Directions

The results have practical implications for applications in areas such as routing, regulatory compliance, and disaster management, where current maps can become obsolete or inaccurate quickly. Furthermore, as global datasets grow in richness and diversity, methodologies from this paper could be extended toward truly global map generation capabilities.

The paper illustrates the potential of leveraging large-scale, albeit imperfect, datasets for deep learning, providing insights into handling noise and incompleteness—common issues in many real-world scenarios. Future directions might include exploring more sophisticated data handling through semi-supervised learning to leverage unlabeled data, or introducing more advanced attention mechanisms within the network architectures to improve feature differentiation further.

Overall, the authors contribute to the expanding frontier of geo-spatial data processing by demonstrating robust methods for map generation through innovative algorithmic solutions and data handling strategies. Such research is foundational as we progress toward automated, accurate cartographic representations.