284 research outputs found
Warming and Crop Production in the US and Beyond
This presentation will discuss what we currently know about how crops respond to warming, where the biggest impacts over the next few decades might be, and what we can do to adapt.Title VI National Resource Center Grant (P015A060066)unpublishednot peer reviewe
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
The lack of reliable data in developing countries is a major obstacle to
sustainable development, food security, and disaster relief. Poverty data, for
example, is typically scarce, sparse in coverage, and labor-intensive to
obtain. Remote sensing data such as high-resolution satellite imagery, on the
other hand, is becoming increasingly available and inexpensive. Unfortunately,
such data is highly unstructured and currently no techniques exist to
automatically extract useful insights to inform policy decisions and help
direct humanitarian efforts. We propose a novel machine learning approach to
extract large-scale socioeconomic indicators from high-resolution satellite
imagery. The main challenge is that training data is very scarce, making it
difficult to apply modern techniques such as Convolutional Neural Networks
(CNN). We therefore propose a transfer learning approach where nighttime light
intensities are used as a data-rich proxy. We train a fully convolutional CNN
model to predict nighttime lights from daytime imagery, simultaneously learning
features that are useful for poverty prediction. The model learns filters
identifying different terrains and man-made structures, including roads,
buildings, and farmlands, without any supervision beyond nighttime lights. We
demonstrate that these learned features are highly informative for poverty
mapping, even approaching the predictive performance of survey data collected
in the field.Comment: In Proc. 30th AAAI Conference on Artificial Intelligenc
Global scale climate–crop yield relationships and the impacts of recent warming
Changes in the global production of major crops are important drivers of food prices, food security and land use decisions. Average global yields for these commodities are determined by the performance of crops in millions of fields distributed across a range of management, soil and climate regimes. Despite the complexity of global food supply, here we show that simple measures of growing season temperatures and precipitation—spatial averages based on the locations of each crop—explain ∼30% or more of year-to-year variations in global average yields for the world’s six most widely grown crops. For wheat, maize and barley, there is a clearly negative response of global yields to increased temperatures. Based on these sensitivities and observed climate trends, we estimate that warming since 1981 has resulted in annual combined losses of these three crops representing roughly 40 Mt or $5 billion per year, as of 2002. While these impacts are small relative to the technological yield gains over the same period, the results demonstrate already occurring negative impacts of climate trends on crop yields at the global scale
Tile2Vec: Unsupervised representation learning for spatially distributed data
Geospatial analysis lacks methods like the word vector representations and
pre-trained networks that significantly boost performance across a wide range
of natural language and computer vision tasks. To fill this gap, we introduce
Tile2Vec, an unsupervised representation learning algorithm that extends the
distributional hypothesis from natural language -- words appearing in similar
contexts tend to have similar meanings -- to spatially distributed data. We
demonstrate empirically that Tile2Vec learns semantically meaningful
representations on three datasets. Our learned representations significantly
improve performance in downstream classification tasks and, similar to word
vectors, visual analogies can be obtained via simple arithmetic in the latent
space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi
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