12,214 research outputs found
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
An integrated study of earth resources in the state of California using remote sensing techniques
There are no author-identified significant results in this report
Analyses of the cloud contents of multispectral imagery from LANDSAT 2: Mesoscale assessments of cloud and rainfall over the British Isles
The author has identified the following significant results. It was demonstrated that satellites with sufficiently high resolution capability in the visible region of the electromagnetic spectrum could be used to check the accuracy of estimates of total cloud amount assessed subjectively from the ground, and to reveal areas of performance in which corrections should be made. It was also demonstrated that, in middle latitude in summer, cloud shadow may obscure at least half as much again of the land surface covered by an individual LANDSAT frame as the cloud itself. That proportion would increase with latitude and/or time of year towards the winter solstice. Analyses of sample multispectral images for six different categories of clouds in summer revealed marked differences between the reflectance characteristics of cloud fields in the visible/near infrared region of the spectrum
Dynamics and Spatial Distribution of Global Nighttime Lights
Using open source data, we observe the fascinating dynamics of nighttime
light. Following a global economic regime shift, the planetary center of light
can be seen moving eastwards at a pace of about 60 km per year. Introducing
spatial light Gini coefficients, we find a universal pattern of human
settlements across different countries and see a global centralization of
light. Observing 160 different countries we document the expansion of
developing countries, the growth of new agglomerations, the regression in
countries suffering from demographic decline and the success of light pollution
abatement programs in western countries
Synthetic aperture radar/LANDSAT MSS image registration
Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint
- …