Kilo-Degree Survey (KiDS), thanks to its depth, excellent imaging and multi-wavelength coverage (jointly with VIKING), allows us to go beyond cosmic shear analyses. I will present how we extract and employ both low- and high-redshift galaxies and quasars from KiDS, taking advantage of its considerable overlap with various spectroscopic calibration datasets, such as GAMA and now also DESI. In selecting these objects and estimating their photometric redshifts, we use in particular machine-learning approaches including deep learning, and I will discuss our recent results in this matter. I will then overview some of the recent applications of these KiDS photometric galaxies and quasars, based on probes such as galaxy-galaxy lensing and clustering. Time permitting, I will also sketch near-future prospects of extending this kind of studies beyond KiDS with new surveys such as 4MOST and LSST.
