  We present a single-dish mapping algorithm with a number of advantages over
traditional techniques. (1) Our algorithm makes use of weighted modeling,
instead of weighted averaging, to interpolate between signal measurements. This
smooths the data, but without blurring the data beyond instrumental resolution.
Techniques that rely on weighted averaging blur point sources sometimes as much
as 40%. (2) Our algorithm makes use of local, instead of global, modeling to
separate astronomical signal from instrumental and/or environmental signal
drift along the telescope's scans. Other techniques, such as basket weaving,
model this drift with simple functional forms (linear, quadratic, etc.) across
the entirety of scans, limiting their ability to remove such contaminants. (3)
Our algorithm makes use of a similar, local modeling technique to separate
astronomical signal from radio-frequency interference (RFI), even if only
continuum data are available. (4) Unlike other techniques, our algorithm does
not require data to be collected on a rectangular grid or regridded before
processing. (5) Data from any number of observations, overlapping or not, may
be appended and processed together. (6) Any pixel density may be selected for
the final image. We present our algorithm, and evaluate it using both simulated
and real data. We are integrating it into the image-processing library of the
Skynet Robotic Telescope Network, which includes optical telescopes spanning
four continents, and now also Green Bank Observatory's 20-meter diameter radio
telescope in West Virginia. Skynet serves hundreds of professional users, and
additionally tens of thousands of students, of all ages. Default data products
are generated on the fly, but will soon be customizable after the fact.
