The Spectrascapes Dataset: Street-view imagery beyond the visible captured using a mobile platform

High-resolution data in spatial and temporal contexts is imperative for developing climate resilient cities. Current datasets for monitoring urban parameters are developed primarily using manual inspections, embedded-sensing, remote sensing, or standard street-view imagery (RGB). These methods and datasets are often constrained respectively by poor scalability, inconsistent spatio-temporal resolutions, overhead views or low spectral information. We present a novel method and its open implementation: a multi-spectral terrestrial-view dataset that circumvents these limitations. This dataset consists of 17,718 street level multi-spectral images captured with RGB, Near-infrared, and Thermal imaging sensors on bikes, across diverse urban morphologies (village, town, small city, and big urban area) in the Netherlands. Strict emphasis is put on data calibration and quality while also providing the details of our data collection methodology (including the hardware and software details). To the best of our knowledge, Spectrascapes is the first open-access dataset of its kind. Finally, we demonstrate two downstream use-cases enabled using this dataset and provide potential research directions in the machine learning, urban planning and remote sensing domains.

Using street view imagery and deep generative modeling for estimating the health of urban forests

Healthy urban forests comprising of diverse trees and shrubs play a crucial role in mitigating climate change. They provide several key advantages such as providing shade for energy conservation, and intercepting rainfall to reduce flood runoff and soil erosion. Traditional approaches for monitoring the health of urban forests require instrumented inspection techniques, often involving a high amount of human labor and subjective evaluations. As a result, they are not scalable for cities which lack extensive resources. Recent approaches involving multi-spectral imaging data based on terrestrial sensing and satellites, are constrained respectively with challenges related to dedicated deployments and limited spatial resolutions. In this work, we propose an alternative approach for monitoring the urban forests using simplified inputs: street view imagery, tree inventory data and meteorological conditions. We propose to use image-to-image translation networks to estimate two urban forest health parameters, namely, NDVI and CTD. Finally, we aim to compare the generated results with ground truth data using an onsite campaign utilizing handheld multi-spectral and thermal imaging sensors. With the advent and expansion of street view imagery platforms such as Google Street View and Mapillary, this approach should enable effective management of urban forests for the authorities in cities at scale.

A Comprehensive Survey of AI-Driven Advancements and Techniques in Automated Program Repair and Code Generation

Bug fixing and code generation have been core research topics in software development for many years. The recent explosive growth in Large Language Models has completely transformed these spaces, putting in reach incredibly powerful tools for both. In this survey, 27 recent papers have been reviewed and split into two groups: one dedicated to Automated Program Repair (APR) and LLM integration and the other to code generation using LLMs. The first group consists of new methods for bug detection and repair, which include locating semantic errors, security vulnerabilities, and runtime failure bugs. The place of LLMs in reducing manual debugging efforts is emphasized in this work by APR toward context-aware fixes, with innovations that boost accuracy and efficiency in automatic debugging. The second group dwells on code generation, providing an overview of both general-purpose LLMs fine-tuned for programming and task-specific models. It also presents methods to improve code generation, such as identifier-aware training, fine-tuning at the instruction level, and incorporating semantic code structures. This survey work contrasts the methodologies in APR and code generation to identify trends such as using LLMs, feedback loops to enable iterative code improvement and open-source models. It also discusses the challenges of achieving functional correctness and security and outlines future directions for research in LLM-based software development.