  Context. Upcoming space missions will provide us with surface-resolved NEA
reflectance spectra. Neural networks are useful tools for analysing reflectance
spectra and determining material composition with high precision and low
processing time. Aims. We applied neural-network models on disk-resolved
spectra of the Eros and Itokawa asteroids observed by the NEAR Shoemaker and
Hayabusa spacecraft. With this approach, the mineral variations or intensity of
space weathering can be mapped. Methods. We tested two types of convolutional
neural networks. The first one was trained using asteroid reflectance spectra
with known taxonomy classes. The other one used silicate reflectance spectra
with assigned mineral abundances and compositions. Results. The reliability of
the classification model depends on the resolution of reflectance spectra.
Typical F1 score and Cohen's ${\kappa}_C$ values decrease from about 0.90 for
high-resolution spectra to about 0.70 for low-resolution spectra. The predicted
silicate composition does not strongly depend on spectrum resolution and
coverage of the 2${\mu}$m band of pyroxene. The typical root mean square error
is between 6 and 10 percentage points. For the Eros and Itokawa asteroids, the
predicted taxonomy classes favour the S-type and the predicted surface
compositions are homogeneous and correspond to the composition of L/LL and LL
ordinary chondrites, respectively. On the Itokawa surface, the model identified
fresh spots that were connected with craters or coarse-grain areas.
Conclusions. The neural network models trained with measured spectra of
asteroids and silicate samples are suitable for deriving surface silicate
mineralogy with a reasonable level of accuracy. The predicted surface
mineralogy is comparable to the mineralogy of returned samples measured in the
laboratory. Moreover, the taxonomical predictions can point out locations of
fresher areas.
