  This work addresses the challenge of training supervised machine or deep
learning models on orbiting platforms where we are generally constrained by
limited on-board hardware capabilities and restricted uplink bandwidths to
upload. We aim at enabling orbiting spacecrafts to (1) continuously train a
lightweight model as it acquires imagery; and (2) receive new labels while on
orbit to refine or even change the predictive task being trained. For this, we
consider chip level regression tasks (i.e. predicting the vegetation percentage
of a 20 km$^2$ patch) when we only have coarser label proportions, such as
municipality level vegetation statistics (a municipality containing several
patches). Such labels proportions have the additional advantage that usually
come in tabular data and are widely available in many regions of the world and
application areas. This can be framed as a Learning from Label Proportions
(LLP) problem setup. LLP applied to Earth Observation (EO) data is still an
emerging field and performing comparative studies in applied scenarios remains
a challenge due to the lack of standardized datasets. In this work, first, we
show how very simple deep learning and probabilistic methods (with
{\raise.17ex\hbox{$\scriptstyle\sim$}}5K parameters) generally perform better
than standard more complex ones, providing a surprising level of finer grained
spatial detail when trained with much coarser label proportions. Second, we
publish a set of benchmarking datasets enabling comparative LLP applied to EO,
providing both fine grained labels and aggregated data according to existing
administrative divisions. Finally, we show how this approach fits an on-orbit
training scenario by reducing vastly both the amount of computing and the size
of the labels sets. Source code is available at
https://github.com/rramosp/llpeo
