  Recent advancements in remote sensing technology have resulted in petabytes
of data in raster format. This data is often processed in combination with high
resolution vector data that represents, for example, city boundaries. One of
the common operations that combine big raster and vector data is the zonal
statistics which computes some statistics for each polygon in the vector
dataset. This paper models the zonal statistics problem as a join problem and
proposes a novel distributed system that can scale to petabytes of raster and
vector data. The proposed method does not require any preprocessing or indexing
which makes it perfect for ad-hoc queries that scientists usually want to run.
We devise a theoretical cost model that proves the efficiency of our algorithm
over the baseline method. Furthermore, we run an extensive experimental
evaluation on large scale satellite data with up-to a trillion pixels, and big
vector data with up-to hundreds of millions of edges, and we show that our
method can perfectly scale to big data with up-to two orders of magnitude
performance gain over Rasdaman and Google Earth Engine.
