  Big research efforts have been devoted to efficiently manage spatio-temporal
data. However, most works focused on vectorial data, and much less, on raster
data. This work presents a new representation for raster data that evolve along
time named Temporal k^2 raster. It faces the two main issues that arise when
dealing with spatio-temporal data: the space consumption and the query response
times. It extends a compact data structure for raster data in order to manage
time and thus, it is possible to query it directly in compressed form, instead
of the classical approach that requires a complete decompression before any
manipulation. In addition, in the same compressed space, the new data structure
includes two indexes: a spatial index and an index on the values of the cells,
thus becoming a self-index for raster data.
