A method for use in generating population density data sets of a geographic region from overhead imagery data, comprising: obtaining a plurality of multi-scale morphological image descriptors for each of a respective plurality of pixels of at least one input overhead image of a geographic region; training, with a processor, a linear model with a) a plurality of population density estimates of a respective plurality of first portions of the geographic region and b) the multi-scale morphological image descriptors within each of the plurality of first portions to obtain a plurality of weighting values, wherein the training step includes: decomposing the multiscale morphological image descriptors within each of the first portions into a respective plurality of first vectors that each include a plurality of entries, wherein each entry includes a value that represents a frequency of one of a plurality of fixed values of the multiscale morphological image descriptors within a respective one of the first portions; combining the plurality of first vectors into a matrix; generating a second vector that includes a plurality of entries, wherein each entry includes a value that represents one of the population density estimates of a respective one of the plurality of first portions of the geographic region, wherein the linear model comprises a difference between a) the second vector and b) a product of the matrix and a third vector that includes a plurality of entries, and wherein the training step further includes: determining a particular plurality of values of the respective plurality of entries of the third vector that minimizes the difference, wherein the particular plurality of values is the plurality of weighting values; and determining, with the processor, a plurality of population density estimates of a respective plurality of second portions of the geographic region using a) the plurality of weighting values and b) the multi-scale morphological image descriptors within each of the plurality of second portions, wherein the second portions are smaller in area than the first portions. obtaining a plurality of multi-scale morphological image descriptors for each of a respective plurality of pixels of at least one input overhead image of a geographic region; training, with a processor, a linear model with a) a plurality of population density estimates of a respective plurality of first portions of the geographic region and b) the multi-scale morphological image descriptors within each of the plurality of first portions to obtain a plurality of weighting values, wherein the training step includes: decomposing the multiscale morphological image descriptors within each of the first portions into a respective plurality of first vectors that each include a plurality of entries, wherein each entry includes a value that represents a frequency of one of a plurality of fixed values of the multiscale morphological image descriptors within a respective one of the first portions; combining the plurality of first vectors into a matrix; generating a second vector that includes a plurality of entries, wherein each entry includes a value that represents one of the population density estimates of a respective one of the plurality of first portions of the geographic region, wherein the linear model comprises a difference between a) the second vector and b) a product of the matrix and a third vector that includes a plurality of entries, and wherein the training step further includes: determining a particular plurality of values of the respective plurality of entries of the third vector that minimizes the difference, wherein the particular plurality of values is the plurality of weighting values; and decomposing the multiscale morphological image descriptors within each of the first portions into a respective plurality of first vectors that each include a plurality of entries, wherein each entry includes a value that represents a frequency of one of a plurality of fixed values of the multiscale morphological image descriptors within a respective one of the first portions; combining the plurality of first vectors into a matrix; generating a second vector that includes a plurality of entries, wherein each entry includes a value that represents one of the population density estimates of a respective one of the plurality of first portions of the geographic region, wherein the linear model comprises a difference between a) the second vector and b) a product of the matrix and a third vector that includes a plurality of entries, and wherein the training step further includes: determining a particular plurality of values of the respective plurality of entries of the third vector that minimizes the difference, wherein the particular plurality of values is the plurality of weighting values; and determining, with the processor, a plurality of population density estimates of a respective plurality of second portions of the geographic region using a) the plurality of weighting values and b) the multi-scale morphological image descriptors within each of the plurality of second portions, wherein the second portions are smaller in area than the first portions.