Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series

• URL: http://arxiv.org/abs/2110.08187v1
• Date: Fri, 15 Oct 2021 16:38:41 GMT
• Title: Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
• Authors: F\'elix Quinton and Loic Landrieu
• Abstract summary: We propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics of parcel classification. Our model provides an improvement of over 6.6 mIoU points over the current state-of-the-art of crop classification. We release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.
• Score: 5.715103211247915
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While annual crop rotations play a crucial role for agricultural optimization, they have been largely ignored for automated crop type mapping. In this paper, we take advantage of the increasing quantity of annotated satellite data to propose the first deep learning approach modeling simultaneously the inter- and intra-annual agricultural dynamics of parcel classification. Along with simple training adjustments, our model provides an improvement of over 6.6 mIoU points over the current state-of-the-art of crop classification. Furthermore, we release the first large-scale multi-year agricultural dataset with over 300,000 annotated parcels.

Related papers

• Generating Diverse Agricultural Data for Vision-Based Farming Applications [74.79409721178489]
  This model is capable of simulating distinct growth stages of plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture.
  arXiv  Detail & Related papers  (2024-03-27T08:42:47Z)
• Foundation Models for Generalist Geospatial Artificial Intelligence [3.7002058945990415]
  This paper introduces a first-of-a-kind framework for the efficient pre-training and fine-tuning of foundational models on extensive data. We have utilized this framework to create Prithvi, a transformer-based foundational model pre-trained on more than 1TB of multispectral satellite imagery.
  arXiv  Detail & Related papers  (2023-10-28T10:19:55Z)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• GEO-Bench: Toward Foundation Models for Earth Monitoring [139.77907168809085]
  We propose a benchmark comprised of six classification and six segmentation tasks. This benchmark will be a driver of progress across a variety of Earth monitoring tasks.
  arXiv  Detail & Related papers  (2023-06-06T16:16:05Z)
• Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data [0.0]
  We propose a novel approach to fuse multimodal information into a model for improved accuracy and robustness across multiple years and countries. To evaluate our approach, we release a new annotated dataset of 7.4 million agricultural parcels in France and Netherlands.
  arXiv  Detail & Related papers  (2023-05-19T21:42:53Z)
• High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production [2.5402662954395097]
  We introduce a first-of-its-kind dataset, SICKLE, having time-series images at different spatial resolutions from 3 different satellites. The dataset comprises of 2,398 season-wise samples from 388 unique plots distributed across 4 districts of the Delta. We benchmark the dataset on 3 separate tasks, namely crop type, phenology date (sowing, transplanting, harvesting) and yield prediction, and develop an end-to-end framework for predicting key crop parameters in a real-world setting.
  arXiv  Detail & Related papers  (2022-09-25T14:54:50Z)
• Multimodal Crop Type Classification Fusing Multi-Spectral Satellite Time Series with Farmers Crop Rotations and Local Crop Distribution [0.0]
  We propose to tackle a land use and crop type classification task using three data types. The Accuracy by 5.1 points in a 28-class setting (.948), and the micro-F1 by 9.6 points in a 10-class setting (.887) using only a set of crop of interests selected by an expert.
  arXiv  Detail & Related papers  (2022-08-23T09:41:09Z)
• Standardizing and Centralizing Datasets to Enable Efficient Training of Agricultural Deep Learning Models [0.0]
  Deep learning models are typically fine-tuned to agricultural tasks using model weights originally fit to more general, non-agricultural datasets. We collect a wide range of existing public datasets for three distinct tasks, standardize them, and construct standard training and evaluation pipelines. We conduct a number of experiments using methods which are commonly used in deep learning tasks, but unexplored in their domain-specific applications for agriculture.
  arXiv  Detail & Related papers  (2022-08-04T15:10:36Z)
• Beyond Transfer Learning: Co-finetuning for Action Localisation [64.07196901012153]
  We propose co-finetuning -- simultaneously training a single model on multiple upstream'' and downstream'' tasks. We demonstrate that co-finetuning outperforms traditional transfer learning when using the same total amount of data. We also show how we can easily extend our approach to multiple upstream'' datasets to further improve performance.
  arXiv  Detail & Related papers  (2022-07-08T10:25:47Z)
• CHALLENGER: Training with Attribution Maps [63.736435657236505]
  We show that utilizing attribution maps for training neural networks can improve regularization of models and thus increase performance. In particular, we show that our generic domain-independent approach yields state-of-the-art results in vision, natural language processing and on time series tasks.
  arXiv  Detail & Related papers  (2022-05-30T13:34:46Z)
• Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation [79.47771259100674]
  We present two sample-adaptive automatic weighting schemes for data augmentation. We validate our proposed methods on a large, noisy financial dataset and on time-series datasets from the UCR archive. On the financial dataset, we show that the methods in combination with a trading strategy lead to improvements in annualized returns of over 50$%$, and on the time-series data we outperform state-of-the-art models on over half of the datasets, and achieve similar performance in accuracy on the others.
  arXiv  Detail & Related papers  (2021-02-16T17:50:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.