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In the era of large sky surveys, photometric redshifts (photo-z) representcrucial information for galaxy evolution and cosmology studies. In this work,we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z withneural Networks (GaZNet-1), which uses both images and multi-band photometrymeasurements to predict galaxy redshifts, with accuracy, precision and outlierfraction superior to standard methods based on photometry only. As a firstapplication of this tool, we estimate photo-z of a sample of galaxies in theKilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on $\sim140 000$galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopicredshifts are available from different surveys. This sample is dominated bybright (MAG$\_$AUTO$<21$) and low redshift ($z < 0.8$) systems, however, wecould use $\sim$ 6500 galaxies in the range $0.8 < z < 3$ to effectively extendthe training to higher redshift. The inputs are the r-band galaxy images plusthe 9-band magnitudes and colours, from the combined catalogs of opticalphotometry from KiDS and near-infrared photometry from the VISTA Kilo-degreeInfrared survey. By combining the images and catalogs, GaZNet-1 can achieveextremely high precision in normalized median absolute deviation (NMAD=0.014for lower redshift and NMAD=0.041 for higher redshift galaxies) and lowfraction of outliers ($0.4$\% for lower and $1.27$\% for higher redshiftgalaxies). Compared to ML codes using only photometry as input, GaZNet-1 alsoshows a $\sim 10-35$% improvement in precision at different redshifts and a$\sim$ 45% reduction in the fraction of outliers. We finally discuss that, bycorrectly separating galaxies from stars and active galactic nuclei, theoverall photo-z outlier fraction of galaxies can be cut down to $0.3$\%.

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry