WinTR-55 is a single-event rainfall-runoff small watershed hydrologic model. The model generates hydrographs from both urban and agricultural areas and at selected points along the stream system. Hydrographs are routed downstream through channels and/or reservoirs. Multiple sub-areas can be modeled within the watershed.
The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated.
Cligen is a stochastic weather generator which produces daily estimates of precipitation, temperature, dewpoint, wind, and solar radiation for a single geographic point, using monthly parameters (means, SD's, skewness, etc.) derived from the historic measurements. Unlike other climate generators, it produces individual storm parameter estimates, including time to peak, peak intensity, and storm duration, which are required to run the WEPP and the WEPS soil erosion models.
The NAL Agricultural Thesaurus (NALT) was first released by the National Agricultural Library in 2002, with in-depth coverage of agriculture, biology, and related disciplines. It contains over 135,000 terms, including 63,000 cross references, and is arranged into 17 subject categories which are used to…
USDA Forest Service Remote Sensing Applications Center and Geospatial Technology and Applications Center
The Forest Service's Remote Sensing Applications Center (RSAC) is in Salt Lake City, Utah, co-located with the agency's Geospatial Service and Technology Center. Guided by national steering committees and field sponsors, RSAC provides national assistance to agency field units in applying the most advanced geospatial technology toward improved monitoring and mapping of natural resources. RSAC's principal goal is to develop and implement less costly ways for the Forest Service to obtain needed forest resource information.
Person detection from vehicles has made rapid progress recently with the advent of multiple high-quality datasets of urban and highway driving, yet no large-scale benchmark is available for the same problem in off-road or agricultural environments. Here we present the National Robotics Engineering Center (NREC) Agricultural Person-Detection Dataset to spur research in these environments. It consists of labeled stereo video of people in orange and apple orchards taken from two perception platforms (a tractor and a pickup truck), along with vehicle position data from Real Time Kinetic (RTK) GPS. We define a benchmark on part of the dataset that combines a total of 76k labeled person images and 19k sampled person-free images. The dataset highlights several key challenges of the domain, including varying environment, substantial occlusion by vegetation, people in motion and in nonstandard poses, and people seen from a variety of distances; metadata are included to allow targeted evaluation of each of these effects.