Offline Bird Call Survey App for Wetland and Wildlife Field Teams

The problem: surveys on paper, birds by ear

Pakistan's wetlands, rivers and forests are important for resident birds and for migratory birds that pass through every winter. Wildlife departments, university ecology groups, conservation organisations and keen birdwatchers survey them, counting what they see and hear at fixed points. Much of this is done on paper. Many birds are heard, not seen, and identifying calls by ear takes years of experience that most volunteers and students do not have.

The result is uneven data: different observers record different things, uncertain identifications are not recorded at all, and nobody can go back and check a doubtful one. For departments that must report on protected sites, and for researchers tracking how migratory birds respond to drought, floods and heat, that unevenness makes year-to-year comparisons unreliable.

What already exists and the gap

• The BirdNET app, from the Cornell Lab of Ornithology and Chemnitz University of Technology, identifies bird sounds from recordings and is used by birdwatchers worldwide.
• Merlin Bird ID, also from the Cornell Lab, helps people identify birds by sound and photo.
• Survey platforms let birdwatchers submit checklists.

These are excellent for individuals. The gap is a survey tool for teams in places without signal: structured point counts, on-device identification filtered to locally expected species, each suggestion confirmed or rejected with the audio kept, and an expert review loop, so the final data is consistent and checkable.

Inspiration

"BirdNET: A deep learning solution for avian diversity monitoring" by Stefan Kahl, Connor Wood, Maximilian Eibl and Holger Klinck, published in Ecological Informatics in 2021, describes a deep learning model that identifies bird species from sound and has since grown into an open toolkit covering thousands of species. Its models are released for non-commercial, research and educational use. Your project builds a field survey workflow around it.

What you build

• Field teams start a survey at a point, record for the set time and confirm or reject each suggested species, all offline.
• Experts review uncertain clips from many surveys in one queue.
• Ecologists and departments export clean, consistent survey data with evidence behind each record.

The app must work for hours on a phone's battery in heat and dust, with a screen readable in sun. Keep the interface to a few large controls, and make it impossible to lose a survey if the app is closed.

The technically interesting part

• Running the model on the phone. Load the open model with TensorFlow Lite, process audio in short windows and keep the phone responsive. Measure speed and battery use.
• Local filtering. A global model may suggest species that never occur in Sindh in May. Build a regional, seasonal species list from published checklists and use it to filter and re-rank suggestions, and measure how much it helps.
• Confidence and review. Decide which suggestions are shown as likely, which go to review and which are hidden. Use the field test to tune thresholds.
• Evidence. Keep only the short clip around each detection, with its location and time, so storage stays small and every record can be checked later.

How to evaluate it

• Suggestion accuracy: at a real site, compare the app's suggestions with an expert's identification of the same recordings, before and after regional filtering.
• Review load: share of detections that needed expert review.
• Battery and storage: use per hour of surveying on a mid-range phone.
• Usability: feedback from a few students or volunteers after a field morning, including whether they would trust the app enough to stop carrying paper forms.
• Data quality: compare the consistency of records from two observers surveying the same point with and without the app.

Suggested plan

• Weeks 1 to 3: read the BirdNET paper, check the model licence, contact a university ecology group or birding club.
• Weeks 4 to 7: recording, on-device identification and survey records.
• Weeks 8 to 10: regional species list, confirm and review flow, export.
• Weeks 11 to 13: field test and evaluation with an expert.
• Weeks 14 to 15: report and demo.

Path to a product

Wildlife departments, environmental consultancies doing impact assessments and conservation organisations all need consistent survey data. A survey app with a paid team dashboard is realistic, provided the model licence allows the use; check it before any commercial step. The project is a clean, real-world mobile machine learning piece.

Scope warning

Respect the model licence: it is for non-commercial use. Do not try to train a new bird sound model. Do not record people's conversations; keep only short clips around detected bird calls. Get permission before surveying protected areas.

Learn what you need on Sefism

• Mobile development roadmap: offline storage, audio and battery-aware design.
• Mobile development cheat sheet: quick reference while you build.
• Machine learning roadmap: thresholds, evaluation and error analysis.
• Android Developers: Develop for Android: audio recording and on-device inference.
• Flutter Documentation: if you build the app in Flutter.

Get help with this idea

The risk is a field test that comes too late to tune anything. Tauseef can help you find a partner group, plan the evaluation and keep the app simple enough for field use. If you are new here, create a free account first.

• Discuss this idea with a mentor
• Send a query to Tauseef
• Book a 1:1 session