Instant crisis information on social media is the most searched essen-tials to provide relief and rescue operations to the victims at the early crisis hours. However, insufficient information on underway crisis incidents and rich data from past crisis events resort to domain adaptation (DA) techniques over any other approaches. Despite, the existing DA methods could not adequately engage the available past resources and hence lose vital information for the ongoing crisis incidents compromising the performance. Existing pitfalls of state-of-the-art models are: (1) models do not work on joint domain feature relation at elementary and instance level to exploit the complete information of each domain (2) moreover, these models could not efficiently harness the information, when there are diversified and varying number of source crisis incidents. Inspired by the ensemble setup in identifying the infrastructure damage, we introduce Ensemble model using the elementary feature (Parts-of-speech tagging) Attention and Hypersphere Separator Springer Nature 2021 L A T E X template Domain Adaptation Approach to Classify Infrastructure Damage (EnPHyS). It operates at joint feature levels where each level works with the abundant source and scarce target data to extract the best of the (1) shared and (2) invariant features for the objective task. Ensemble uses Multi-Task Learning (MTL) and an adversarial approach to enhance the information retrieval of target features. EnPHyS performance was investigated under single-source as well as multi-source domain adaptation scenarios with four publicly available datasets. The reported results on standard metric F-measure reveal the average growth of 17%, 22% and 38% respectively over the best performing baseline model.