Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery (2504.02534v1)

Abstract: The accurate delineation of agricultural field boundaries from satellite imagery is vital for land management and crop monitoring. However, current methods face challenges due to limited dataset sizes, resolution discrepancies, and diverse environmental conditions. We address this by reformulating the task as instance segmentation and introducing the Field Boundary Instance Segmentation - 22M dataset (FBIS-22M), a large-scale, multi-resolution dataset comprising 672,909 high-resolution satellite image patches (ranging from 0.25 m to 10 m) and 22,926,427 instance masks of individual fields, significantly narrowing the gap between agricultural datasets and those in other computer vision domains. We further propose Delineate Anything, an instance segmentation model trained on our new FBIS-22M dataset. Our proposed model sets a new state-of-the-art, achieving a substantial improvement of 88.5% in [email protected] and 103% in [email protected]:0.95 over existing methods, while also demonstrating significantly faster inference and strong zero-shot generalization across diverse image resolutions and unseen geographic regions. Code, pre-trained models, and the FBIS-22M dataset are available at https://lavreniuk.github.io/Delineate-Anything.

An Expert Overview of "Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery"

The paper "Delineate Anything: Resolution-Agnostic Field Boundary Delineation on Satellite Imagery" introduces a novel approach to agricultural field boundary detection using satellite imagery. The authors propose a method centered around the concept of instance segmentation, specifically employing a model called Delineate Anything, which leverages a new large-scale dataset dubbed the Field Boundary Instance Segmentation - 22M (FBIS-22M). This dataset is pivotal in achieving state-of-the-art results and overcoming current challenges in this domain.

The significance of accurate agricultural field boundary delineation from satellite imagery cannot be overstated as it plays a vital role in land management and precision agriculture. Notably, existing methods have struggled with limitations stemming from dataset sizes, the resolution of images, and variability in environmental conditions. The research presented in this paper recasts the task into an instance segmentation framework, distinct from the traditional semantic segmentation, thus addressing inherent limitations such as boundary misalignment and the merging of adjacent fields which are common pitfalls in merge-based semantic approaches.

Contributions

The primary innovation in this work is threefold:

1. Reformulation as Instance Segmentation: By reimagining field boundary detection as an instance segmentation task, the paper moves beyond treating pixel-level classifications to identifying distinct field instances. This not only aligns practical agricultural needs with algorithmic precision but also significantly enhances the handling of complex field shapes.
2. Introduction of FBIS-22M Dataset: The FBIS-22M dataset is a cornerstone of this research, featuring over 22 million instance masks across 672,909 high-resolution image patches. It represents an unprecedented scale and diversity, supporting resolutions from 0.25 meters to 10 meters and incorporating data from a variety of satellite sources such as Sentinel-2, Planet, Maxar, and more. This extensive dataset mitigates issues related to model generalization and performance seen with smaller datasets.
3. Development of Delineate Anything Model: The Delineate Anything model showcases a sophisticated instance segmentation capability, achieving superior accuracy and efficiency in field delineation tasks across varied resolutions and geographic landscapes. It sets new benchmarks with 88.5% improvement in mean Average Precision (mAP) at 0.5 IoU, and 103% improvement in mAP from 0.5 to 0.95 IoU over previous state-of-the-art methods.

Evaluation and Comparative Analysis

The Delineate Anything model is rigorously tested against existing methods, including multi-task learning frameworks and foundational models like the Segment Anything Model by Kirillov et al. Inference speeds are substantially optimized, with Delineate Anything significantly faster, thus enhancing practical usability in real-time applications. The zero-shot generalization further underscores its robustness, demonstrating applicability in geographically distinct locations not present in the training set, such as regions in Brazil and Rwanda.

Implications and Future Directions

In terms of practical applications, this research provides a pathway to scalable, automated field boundary detection crucial for modern agriculture, enabling better land usage management and policy-making. Theoretically, it underscores the potential of reframing large-scale segmentation tasks through instance-based approaches, thereby setting a precedent for other domains facing similar challenges.

Future work may explore further enhancements in model efficiency and generalization capabilities, potentially integrating additional modalities of data or leveraging advancements in machine learning architectures to extend the zero-shot learning capabilities even further. As the field progresses, there is a potential to explore broader impacts, including integration with geographic information systems (GIS) and further adaptation to diverse agricultural practices globally.

In summary, the paper offers substantial advancements in field boundary detection, backed by comprehensive methodological innovations and empirical validation, making it a significant contribution to remote sensing and agricultural informatics.