Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types

• URL: http://arxiv.org/abs/2309.05930v2
• Date: Wed, 31 Jan 2024 16:11:27 GMT
• Title: Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types
• Authors: Jordi Laguarta Soler, Thomas Friedel, Sherrie Wang
• Abstract summary: We develop an automated system to generate crop type ground references using deep learning and Google Street View imagery. In Thailand, the resulting country-wide map of rice, cassava, maize, and sugarcane achieves an accuracy of 93%. This is the first time a 10m-resolution, multi-crop map has been created for any smallholder country.
• Score: 3.2437298521021876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate crop type maps are an essential source of information for monitoring yield progress at scale, projecting global crop production, and planning effective policies. To date, however, crop type maps remain challenging to create in low and middle-income countries due to a lack of ground truth labels for training machine learning models. Field surveys are the gold standard in terms of accuracy but require an often-prohibitively large amount of time, money, and statistical capacity. In recent years, street-level imagery, such as Google Street View, KartaView, and Mapillary, has become available around the world. Such imagery contains rich information about crop types grown at particular locations and times. In this work, we develop an automated system to generate crop type ground references using deep learning and Google Street View imagery. The method efficiently curates a set of street view images containing crop fields, trains a model to predict crop type by utilizing weakly-labelled images from disparate out-of-domain sources, and combines predicted labels with remote sensing time series to create a wall-to-wall crop type map. We show that, in Thailand, the resulting country-wide map of rice, cassava, maize, and sugarcane achieves an accuracy of 93%. We publicly release the first-ever crop type map for all of Thailand 2022 at 10m-resolution with no gaps. To our knowledge, this is the first time a 10m-resolution, multi-crop map has been created for any smallholder country. As the availability of roadside imagery expands, our pipeline provides a way to map crop types at scale around the globe, especially in underserved smallholder regions.

Related papers

• Statewide Visual Geolocalization in the Wild [25.978498270475747]
  This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of aerial reference imagery. We partition the search region into geographical cells and train a model to map cells and corresponding photos into a joint embedding space that is used to perform retrieval at test time. Experiments demonstrate that the method successfully localizes 60.6% of all non-panoramic street-view photos uploaded to the crowd-sourcing platform Mapillary in the state of Massachusetts to within 50m of their ground-truth location.
  arXiv  Detail & Related papers  (2024-09-25T09:18:19Z)
• 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)
• The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture [0.8602553195689513]
  temporal patch-based dataset of Canadian croplands enriched with labels retrieved from the Canadian Annual Crop Inventory. The dataset contains 78,536 manually verified high-resolution spatial images from 10 crop classes collected over four crop production years. As a benchmark, we provide models and source code that allow a user to predict the crop class using a single image (ResNet, DenseNet, EfficientNet) or a sequence of images (LRCN, 3D-CNN) from the same location.
  arXiv  Detail & Related papers  (2023-05-31T18:40:15Z)
• Inspecting the Geographical Representativeness of Images from Text-to-Image Models [52.80961012689933]
  We measure the geographical representativeness of generated images using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India. The overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive.
  arXiv  Detail & Related papers  (2023-05-18T16:08:11Z)
• Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes [53.53712888703834]
  We introduce an end-to-end transformer-based architecture that exploits the relationship between different geographic levels. We achieve state of the art street level accuracy on 4 standard geo-localization datasets.
  arXiv  Detail & Related papers  (2023-03-07T21:47:58Z)
• Saliency Guided Contrastive Learning on Scene Images [71.07412958621052]
  We leverage the saliency map derived from the model's output during learning to highlight discriminative regions and guide the whole contrastive learning. Our method significantly improves the performance of self-supervised learning on scene images by +1.1, +4.3, +2.2 Top1 accuracy in ImageNet linear evaluation, Semi-supervised learning with 1% and 10% ImageNet labels, respectively.
  arXiv  Detail & Related papers  (2023-02-22T15:54:07Z)
• Towards Global Crop Maps with Transfer Learning [0.0]
  Deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH-1 time-series. Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.
  arXiv  Detail & Related papers  (2022-11-09T09:17:42Z)
• Using Machine Learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan Region [0.28675177318965034]
  We develop an ML pipeline that relies on Sentinel-2 satellite images time series. We generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.
  arXiv  Detail & Related papers  (2022-03-28T12:08:06Z)
• Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study [82.02173199363571]
  We introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images, accounting for more than 200 billions samples (pixels) We present tools for data representativeness analysis in terms of appearance similarity and a thorough study of MiniFrance data, demonstrating that it is suitable for learning and generalizes well in a semi-supervised setting.
  arXiv  Detail & Related papers  (2020-10-15T15:36:58Z)
• Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis [110.30849704592592]
  We present Agriculture-Vision: a large-scale aerial farmland image dataset for semantic segmentation of agricultural patterns. Each image consists of RGB and Near-infrared (NIR) channels with resolution as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers.
  arXiv  Detail & Related papers  (2020-01-05T20:19:33Z)

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.