We　address　the　problem　of　learning　new　classes　for　semantic　segmentation　models　from　few　examples,　which　is　challenging　because　of　the　following　two　reasons.　Firstly,　it　is　difficult　to　learn　from　limited　novel　data　to　capture　the　underlying　class　distribution.　Secondly,　it　is　challenging　to　retain　knowledge　for　existing　classes　and　to　avoid　catastrophic　forgetting.　For　learning　from　limited　data,　we　propose　a　pseudo-labeling　strategy　to　augment　the　few-shot　training　annotations　in　order　to　learn　novel　classes　more　effectively.　Given　only　one　or　a　few　images　labeled　with　the　novel　classes　and　a　much　larger　set　of　unlabeled　images,　we　transfer　the　knowledge　from　labeled　images　to　unlabeled　images　with　a　coarse-to-fine　pseudo-labeling　approach　in　two　steps.　Specifically,　we　first　match　each　labeled　image　to　its　nearest　neighbors　in　the　unlabeled　image　set　at　the　scene　level,　in　order　to　obtain　images　with　a　similar　scene　layout.　This　is　followed　by　obtaining　pseudo-labels　within　this　neighborhood　by　applying　classifiers　learned　on　the　few-shot　annotations.　In　addition,　we　use　knowledge　distillation　on　both　labeled　and　unlabeled　data　to　retain　knowledge　on　existing　classes.　We　integrate　the　above　steps　into　a　single　convolutional　neural　network　with　a　unified　learning　objective.　Extensive　experiments　on　the　Cityscapes　and　KITTI　datasets　validate　the　efficacy　of　the　proposed　approach　in　the　self-driving　domain.　Code　is　available　from　https://github.com/ChasonJiang/FSCILSS.　©　2023　IEEE.