Monitoring soil health at scale requires tools that are both scientifically robust and computationally efficient. Accurate and efficient estimation of soil chemical properties is essential for sustainable agricultural practices and environmental management. Traditional laboratory-based soil testing methods are labor-intensive, time-consuming, and often impractical for large-scale analysis. Although mid-infrared (MIR) spectroscopy offers a rapid and cost-effective alternative, conventional modeling techniques suffer from inconsistent predictive performance across diverse soil properties. To address this gap, we introduce FTIRNet, a novel multi-task Transformer-based architecture designed to predict multiple soil chemical properties from Fourier-transform infrared (FTIR) spectral data. FTIRNet employs a shared encoder block with task-specific Transformer encoders, enabling the model to learn both general and task-focused spectral representations. These multi-task Transformer encoders incorporate science-informed task relation learning through specialized fusion layers that capture known biogeochemical interdependencies among soil properties. Across a broad set of soil properties, FTIRNet demonstrates consistently superior predictive accuracy compared to traditional approaches.

Fire Departments conduct inspections to prevent fires but it is unclear how to best allocate their limited inspection resources across the properties in a city. Currently, they use their intuition and experience to decide on which properties to inspect and lack a data-driven approach that could lead to a more principled use of inspection resources. The main contribution of this paper is to investigate such an approach, based on machine learning for predicting a fire risk score for properties in a city based on historical fire-incident data. These scores can then be used to help prioritize inspection resources toward higher-risk properties. We present a case study using data from a South Dakota fire department which contains information about properties in a city along with records of fire in- incidents. We use this data consisting of more than 72,000 properties to train a machine learning model to predict fire risk and evaluate its ability to rank the fire risk of properties in the city. We conduct and analyze experiments with variations of XG-Boost, which is an algorithm well-suited to the challenges in application, including missing data and a highly-skewed class distribution. Our evaluation of the model-generated rankings, based on ranking metrics, shows that the model significantly outperforms random rankings and other natural baselines. We also analyze the feature importance computed for the models, which provides further insight into the model behavior. This model has been integrated into an interface for displaying the rankings across a city and is ready for beta testing.