Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multi- spectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain-specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.

Assessing and understanding the urban scale impacts of extreme climate events is a global necessity. Risks associated with heat, where intra-urban dynamics and rural/urban boundary conditions greatly impact its distribution, are of particular interest as the evolution of climate change and ur-banization persists. Characterizing Urban Heat Island (UHI) effects is dependent on the availability of high-resolution near-surface air temperature maps and a description of the Local Climate Zones (LCZs). This study assesses the applicability of state-of-the-art (SOTA) Artificial Intelligence (AI) techniques for UHI detection and characterization. A Geospatial Foundation Model (GFM) is fine-tuned to predict 2 m air temperature at a 1 km resolution for the urban areas of Johannesburg, South Africa, with mean absolute error measures less than 1.5 °C. UHI characterization is further enabled through a Fully Connected Network (FCN) model for LCZs classification for the same region of interest.