arXiv subjects

James Brock

Publications and source records attributed to James Brock.

5 recordsLinked to original sources

arXiv2026

Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis

The increasing availability of high-resolution satellite imagery, together with advances in deep learning, creates new opportunities for forest monitoring workflows. Two central challenges in this domain are pixel-level change detection and semantic change interpretation, particularly for complex forest dynamics. While large language models (LLMs) are increasingly adopted for data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored, especially beyond urban environments. This paper introduces Forest-Chat, an LLM-driven agent for forest change analysis, enabling natural language querying across multiple RSICI tasks, including change detection and captioning, object counting, deforestation characterisation, and change reasoning. Forest-Chat builds upon a multi-level change interpretation (MCI) vision-language backbone with LLM-based orchestration, incorporating zero-shot change detection via AnyChange and multimodal LLM-based zero-shot change captioning and refinement. To support adaptation and evaluation in forest environments, we introduce the Forest-Change dataset, comprising bi-temporal satellite imagery, pixel-level change masks, and semantic change captions via human annotation and rule-based methods. Forest-Chat achieves mIoU and BLEU-4 scores of 67.10% and 40.17% on Forest-Change, and 88.13% and 34.41% on LEVIR-MCI-Trees, a tree-focused subset of LEVIR-MCI. In a zero-shot capacity, it achieves 60.15% and 34.00% on Forest-Change, and 47.32% and 18.23% on LEVIR-MCI-Trees. Further experiments demonstrate the value of caption refinement for injecting geographic domain knowledge into supervised captions, and the system's limited label domain transfer onto JL1-CD-Trees. These findings demonstrate that interactive, LLM-driven systems can support accessible and interpretable forest change analysis.

cs.CV↗

arXiv2026

Vision-Language Agents for Interactive Forest Change Analysis

Modern forest monitoring workflows increasingly benefit from the growing availability of high-resolution satellite imagery and advances in deep learning. Two persistent challenges in this context are accurate pixel-level change detection and meaningful semantic change captioning for complex forest dynamics. While large language models (LLMs) are being adapted for interactive data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored. To address this gap, we introduce an LLM-driven agent for integrated forest change analysis that supports natural language querying across multiple RSICI tasks. The proposed system builds upon a multi-level change interpretation (MCI) vision-language backbone with LLM-based orchestration. To facilitate adaptation and evaluation in forest environments, we further introduce the Forest-Change dataset, which comprises bi-temporal satellite imagery, pixel-level change masks, and multi-granularity semantic change captions generated using a combination of human annotation and rule-based methods. Experimental results show that the proposed system achieves mIoU and BLEU-4 scores of 67.10% and 40.17% on the Forest-Change dataset, and 88.13% and 34.41% on LEVIR-MCI-Trees, a tree-focused subset of LEVIR-MCI benchmark for joint change detection and captioning. These results highlight the potential of interactive, LLM-driven RSICI systems to improve accessibility, interpretability, and efficiency of forest change analysis. All data and code are publicly available at https://github.com/JamesBrockUoB/ForestChat.

cs.CV↗

arXiv2025

Polarizing 3He via Metastability Exchange Optical Pumping Using a 1.2 mbar Sealed Cell at Magnetic Fields up to 5 T

We report high nuclear polarization of 1.2 mbar 3He gas in a sealed cell in magnetic fields up to 5 T using Metastability Exchange Optical Pumping (MEOP). The creation of a highly polarized 3He gas target for use in the 5 T field of Jefferson Lab's CLAS12 spectrometer would enable new studies of spin-dependent asymmetries on the neutron. A systematic study was conducted to evaluate the effects of discharge intensity, pump laser power, and optical pumping transition schemes on nuclear polarization and pumping rates. Steady-state polarizations up to 86 % in magnetic fields between 2 and 5 T were achieved, with a discharge-on relaxation time of 898 s at 5 T. These results underscore the potential of MEOP for high-field applications in nuclear physics experiments.

physics.ins-det↗

arXiv2022

Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey

Satellite Image Time Series (SITS) of the Earth's surface provide detailed land cover maps, with their quality in the spatial and temporal dimensions consistently improving. These image time series are integral for developing systems that aim to produce accurate, up-to-date land cover maps of the Earth's surface. Applications are wide-ranging, with notable examples including ecosystem mapping, vegetation process monitoring and anthropogenic land-use change tracking. Recently proposed methods for SITS classification have demonstrated respectable merit, but these methods tend to lack native mechanisms that exploit the temporal dimension of the data; commonly resulting in extensive data pre-processing contributing to prohibitively long training times. To overcome these shortcomings, Temporal CNNs have recently been employed for SITS classification tasks with encouraging results. This paper seeks to survey this method against a plethora of other contemporary methods for SITS classification to validate the existing findings in recent literature. Comprehensive experiments are carried out on two benchmark SITS datasets with the results demonstrating that Temporal CNNs display a superior performance to the comparative benchmark algorithms across both studied datasets, achieving accuracies of 95.0\% and 87.3\% respectively. Investigations into the Temporal CNN architecture also highlighted the non-trivial task of optimising the model for a new dataset.

cs.CV↗

arXiv2013

Dynamically polarized target for the g2p and GEp experiments at Jefferson Lab

We describe a dynamically polarized target that has been utilized for two electron scattering experiments in Hall A at Jefferson Lab. The primary components of the target are a new, high cooling power 4He evaporation refrigerator, and a re-purposed, superconducting split-coil magnet. It has been used to polarize protons in irradiated NH3 at a temperature of 1 K and at fields of 2.5 and 5.0 Tesla. The performance of the target material in the electron beam under these conditions will be discussed. Maximum polarizations of 28% and 95% were obtained at those fields, respectively. To satisfy the requirements of both experiments, the magnet had to be routinely rotated between angles of 0, 6, and 90 degrees with respect to the incident electron beam. This was accomplished using a new rotating vacuum seal which permits rotations to be performed in only a few minutes.

physics.ins-det↗