Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries

Mei, Weiye; Wang, Haoyu; Fouhey, David F.; Zhou, Weiqi; Hinks, Isabella; Gray, Joseph V.; Berkel, Derek Van; Jain, Meha

doi:10.3390/rs14133046

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…The superpixel and per-pixel methods require that precise plot boundaries are known, whereas the centred method only requires the centre of the plot, making the centred method the most flexible. Additionally, most existing works at a similar scale pose per-pixel problems [22,24]. Thus, our per-pixel models are most appropriate for transfer learning between our task and other remote sensing tasks.…”

Section: Discussionmentioning

confidence: 99%

“…There now exist works which resolved counties at hundreds of metres per pixel [18], fields at tens of metres per pixel [19], and individual trees at <1 m per pixel [20,21]. Operating at a sub-metre pixel resolution has created new opportunities for detecting and describing crops, fields, and farm infrastructure with unprecedented precision [22][23][24]. In particular for this work, the plots used in crop trials are visually distinct in these highest-resolution satellite images.…”

Section: Introductionmentioning

confidence: 99%

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High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

Victor,

Nibali,

Newman

et al. 2024

Remote Sensing

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To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

Victor,

Nibali,

Newman

et al. 2024

Remote Sensing

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“…The quantity of VHR imagery is significantly smaller than that of natural environment images. Classic CNNs often enhance accuracy by increasing the number of convolutional layers, using smaller convolution kernels, and incorporating multiple residuals 7 , 8 . However, this approach to increasing network depth and complexity can exacerbate overfitting issues, especially in situations with limited data where models might overfit to training data excessively.…”

Section: Introductionmentioning

confidence: 99%

Local feature acquisition and global context understanding network for very high-resolution land cover classification

Li,

Hu,

et al. 2024

Sci Rep

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Very high-resolution remote sensing images hold promising applications in ground observation tasks, paving the way for highly competitive solutions using image processing techniques for land cover classification. To address the challenges faced by convolutional neural network (CNNs) in exploring contextual information in remote sensing image land cover classification and the limitations of vision transformer (ViT) series in effectively capturing local details and spatial information, we propose a local feature acquisition and global context understanding network (LFAGCU). Specifically, we design a multidimensional and multichannel convolutional module to construct a local feature extractor aimed at capturing local information and spatial relationships within images. Simultaneously, we introduce a global feature learning module that utilizes multiple sets of multi-head attention mechanisms for modeling global semantic information, abstracting the overall feature representation of remote sensing images. Validation, comparative analyses, and ablation experiments conducted on three different scales of publicly available datasets demonstrate the effectiveness and generalization capability of the LFAGCU method. Results show its effectiveness in locating category attribute information related to remote sensing areas and its exceptional generalization capability. Code is available at https://github.com/lzp-lkd/LFAGCU.

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“…> REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < [27] used Mask R-CNN and WorldView-3 satellite images to better delineate the cropland boundaries in northeast India. However, these methods suffer from problems such as inaccurate extraction of shared edges and omission detection of fuzzy boundaries, failing to meet accuracy requirements in urban management.…”

mentioning

confidence: 99%

A Fuzzy-Boundary Enhanced Trident Network for Parcel Extraction in the Urban–Rural Area

Zhu

Chai

Song

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As the basic unit of farmland, parcel is crucial for remote sensing tasks, such as urban management. Previous studies of farmland parcels extraction are based on boundary detection and instance segmentation methods. However, these methods perform poorly in the parcels with complex shape and fuzzy boundary due to the insufficient feature extraction capability. Moreover, for the lack of multi-scale features extraction and fusion, they are difficult to extract different scale farmland parcels accurately. Based on these issues, we propose a Fuzzy-Boundary Enhanced Trident Network, named FBETNet, to enhance the feature of fuzzy boundary and generate multi-scale parcels. First, a semantic-guided multi-task strategy is introduced in order to enhance the feature of fuzzy boundary. Second, we design a multiscale trident module to further improve the performance of multiscale feature extraction. Finally, a adversarial data augmentation strategy is employed in the training phase to strengthen the robustness and stability of out proposed method. Experiments show that our proposed method improves significantly in both accuracy and visualization, especially for the parcels with fuzzy boundary and complex shape.

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Using Deep Learning and Very-High-Resolution Imagery to Map Smallholder Field Boundaries

Cited by 10 publications

References 44 publications

High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

Local feature acquisition and global context understanding network for very high-resolution land cover classification

A Fuzzy-Boundary Enhanced Trident Network for Parcel Extraction in the Urban–Rural Area

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