ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing

Hao Zhang; Hengrui Liu; Lina Sun; Junmin Meng

cs.AIOct 15, 2026

ISW-Agent: Integrating LLM-Driven Dialogue with n8n Workflow for Internal Solitary Wave Remote Sensing Image Processing

Authors: Hao Zhang, Hengrui Liu, Lina Sun, Junmin Meng

Organizations: First Institute of Oceanography, Ministry of Natural Resources, Qingdao, China

Abstract

Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and propagation prediction. Here, we present ISW-Agent, which integrates large language model (LLM) interpretation with n8n workflows and independent scientific models through structured task constraints, geospatial transformations, and stage-specific output validation. In tests of 120 Chinese instructions per backend, GPT-5.6 Sol and DeepSeek both achieved a strict task-match rate of 94.2%, with all intents and execution decisions matching the reference. Sentinel-1 cases demonstrated continuous processing from dialogue requests to wavefront prediction for the specified scene. Of 50 timed scenes, 39 completed detection, extraction, and prediction with a median turnaround time of 48.04 s; 11 stopped after empty extraction. Across 3,408 controlled fault-injection runs, retries recovered 234 transient-failure conditions and output validation rejected 234 structural defects, while 61 provenance or completeness errors indicated the need for content-consistency checks. Overall, ISW-Agent thus connects natural-language requests to traceable spatial processing chains for multimodel ISW analysis.

Figures & tables

Figure 1: Three-layer architecture of ISW-Agent: dialogue understanding, n8n-based domain-aware orchestration, and scientific model services. Nodes represent logical modules.

Intent	Operation	Required fields	Representative optional fields	Primary output
INT-01	Image download	sensor, region, time_start, time_end	Satellite, product type, polarization, orbit direction	Raw image paths
INT-02	Preprocessing	Input source	Resolution, speckle filter, land mask, output CRS, clipping region	Preprocessed image paths
INT-03	ISW detection	Input source	Confidence threshold, NMS IoU threshold, model variant, output format	Detection results
INT-04	Stripe extraction	Input image	Model variant, output format	Segmentation mask
INT-05	Propagation prediction	Stripe data, target identifier, forecast interval	Environmental data, output format	Predicted trajectories

Table 1: Task contracts for the five ISW-Agent intents.

Figure 2: Simplified ISW-Agent interface and processing of an archived GEE image. (a) Natural-language interpretation and n8n execution status. (b) Image information and key processing parameters. (c) The Sentinel-1A SAR quicklook acquired over the Maluku Sea on 17 May 2026 and archived after GEE download, with the corresponding ISW detection, stripe extraction, and 3 h propagation prediction.

Backend	Valid responses	Strict matches	Correct decisions	Slot F1	Latency (s)
GPT-5.6 Sol	120	113	120	0.978	1.73
DeepSeek	120	113	120	0.978	2.93
Kimi	27	26	26	0.307	7.90
Doubao	95	95	95	0.815	15.70

Table 2: Results for 120 requests per backend. Strict matches and correct decisions are counts. Slot F1 includes all requests. Latency is the median for valid responses.

Figure 3: Dialogue-understanding results for 120 Chinese instructions. The upper panels show intent confusion matrices: D, detection; E, extraction; P, prediction; OOD, out-of-domain request; and NR, no valid response. The lower panels show intent accuracy, slot F1, and task-match rate.

Figure 4: ISW processing for a Sentinel-1A SAR image acquired over the Maluku Sea at 21:17 UTC on 6 March 2026. (a) Processed SAR image. (b) ISW detections, labeled by target identifier. (c) Stripe polygon boundaries and initial crestlines. (d) The initial ISW 5 crestline and its 4 h forecast front.

Figure 5: ISW detection, stripe extraction, and propagation prediction in the Andaman and Celebes Seas. (a) Sentinel-1A SAR image and detections in the Andaman Sea on 23 December 2019. (b) Corresponding extraction results and 4 h and 8 h forecast fronts for ISW 1 and ISW 2. (c) Sentinel-1A SAR image and detections in the Celebes Sea on 22 August 2024. (d) Corresponding extraction results and 4 h and 8 h forecast fronts for ISW 1.

Confidence	NMS IoU 0.30	NMS IoU 0.50	NMS IoU 0.70
0.07	21	25	36
0.15	9	11	17
0.25	5	5	7

Table 3: Total detected instances across 12 scenes under nine threshold combinations.

Figure 6: n8n execution times for 50 Sentinel-1 scenes. The horizontal axis gives the scene index. Stacked segments show detection, extraction, prediction, and remaining intervals, including failed attempts. Crosses mark the 11 scenes that stopped after empty extraction.

Figure 7: Strategy ablation and terminal outcomes under controlled faults. Each column contains the same 852 conditions. Transient(k) combines HTTP 503, HTTP 429, and timeouts, with k denoting consecutive failures. Response comprises two response-error types; Artifact comprises six structural defects; and Content comprises wrong-scene outputs and undeclared feature loss.

Remote Sensing Image Understanding

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Multimodal RAG Multimodal Large Language Models Remote Sensing Tool-Using Vision-Language Agents

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Recent advances in Multimodal Large Language Models (MLLMs) have shown promise for remote sensing tasks such as visual question answering and scene understanding. However, existing models remain limited to basic instruction-following and struggle with real-world scenarios that require multi-source data integration, fine-grained spatial reasoning, and domain expertise. To address this gap, we propose RS-Agent, a domain-adapted intelligent agent that connects user intent with professional remote sensing workflows through structured task planning and tool orchestration. RS-Agent consists of four components aligned with typical remote sensing workflows: a Central Controller for intent understanding and process planning, a dynamic toolkit for tool execution, a Solution Space for task-specific expert guidance, and a Knowledge Space for domain knowledge support. We further introduce Task-Aware Retrieval, which improves planning by identifying task types and retrieving expert-defined solutions, and DualRAG, a weighted dual-path retrieval-augmented generation method that enhances the relevance and completeness of retrieved knowledge. RS-Agent natively supports multiple imaging modalities, including optical and SAR imagery, and can automatically organize dedicated SAR processing tools into executable workflows. Experiments on 9 datasets and 18 remote sensing tasks show that RS-Agent significantly outperforms state-of-the-art MLLMs, achieving over 95% task planning accuracy and strong results in scene classification, object counting, and remote sensing visual question answering. These results demonstrate the value of combining LLM reasoning with remote sensing expertise for intelligent geospatial analysis.

Abstract

Remote Sensing Image Understanding

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