Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagery

EarthSight is a newly proposed distributed framework aimed at enhancing the low-latency delivery of satellite imagery, crucial for applications like disaster response and infrastructure monitoring. Traditional methods face significant delays due to bandwidth limitations, often taking hours to days for image analysis. EarthSight addresses these issues by employing onboard machine learning to prioritize image transmission and redefining satellite image intelligence as a distributed decision-making process between orbit and ground.

MicroVQA++ is a newly introduced high-quality microscopy reasoning dataset designed for multimodal large language models (MLLMs). It is derived from the BIOMEDICA archive and consists of a three-stage process that includes expert-validated figure-caption pairs, a novel heterogeneous graph for filtering inconsistent samples, and human-checked multiple-choice questions. This dataset aims to enhance scientific reasoning in biomedical imaging, addressing the current limitations due to the lack of large-scale training data.

Geospatial chain of thought (CoT) reasoning is crucial for enhancing Visual Question Answering (VQA) on satellite imagery, especially in climate-related applications like disaster monitoring and urban resilience planning. Current VQA models can interpret remote sensing data but often lack the structured reasoning needed for complex geospatial queries. A new framework integrating CoT reasoning with Direct Preference Optimization (DPO) has been proposed, showing a 34.9% accuracy improvement in handling tasks such as detection and classification.